1-s2.0-0038012183900022-main_2

A GOAL PROGRAMMING APPROACH TO PUBLIC INVESTMENT DECISIONS: A CASE STUDY OF

RURAL ROADS IN INDONESIA

THOMAS R. LEINBACH and ROBERT G. CROMLEY Department of Geography. The University of Kentucky, Lexington. KY 40506, U.S.A

(Rrceiwd 30 Ju1.v 1982)

Abstract-The development planner must often face complex problems with multiple. conflictmg objectlves. Goal programming provides a general methodology for solving such problems. The tool IS applied here to aId m the selectlon of rural road projects m the Indonesian Rural Works Program. Selection criteria are formalized mto a set of nineteen goals which form the basis for a goal programming model Changes in priority levels of goals and weights are used to analyze the respective effects upon the spatial distribution of investments. The approach is applicable to a wide range of problems and a variety of sensltlvlty analyses. Deqplte clear advantages. several drawbacks must be noted First, the application of the methodology. given Its degree of sophlstlcatlon. IS hmlted to a central declslon making unit which has access to appropriate software. Second, the technique assumes that the planner has the ability to formulate alternative actlons and consequences m a quantifiable expression.

The strong concern for eradicating rural poverty and eliminating regional development inequItIes has stim- ulated considerable aid transfers as well as international and domestic investment in less developed nations m recent years. The matter of how and where to intervene involves some very complex questions and decisions. Not the least of these concerns is the choice and refinement of pohcy instruments. A basic overriding issue in the development process, of course, is how can we derive the maximum benefits from limited resources and with select opportunities. Defining benefits and deriving maximum utility from investments is simple enough on the surface but very difficult to interpret, implement, and continues to perplex planners. The efficient evaluation of alternative plans for public investment 1s a small yet significant facet of the formulation of regional and national development plans and policies [ I. 21.

APPROACHES TO THE EVALUATION OF ALTERNATIVE PLANS

Derived from the theory of the firm in the 194Os, traditional cost benefit analysis was developed as a technique to evaluate alternaive courses of action under the single goal of economic efficiency usually defined as profit maximization or contribution to national income. Applicable to both the private en- trepreneur and the public agency, cost benefit analysis allocates scarce resources by developing projects m- sofar as marginal revenues exceed or equal marginal costs. Despite its wide usage. the problems in the application, however, often outweigh the advantages. Barriers to the flow of funds, determining the values of benefits and costs in real market prices, and external effects are only several major obstcles to the tech- niques usage[3,4]. An early attempt to surmount these obstacles and pose an alternative solution was suggested by Hill[5]

The evaluation of low volume (less than 100 vehicles per day) roads in developing countries is often especially difficult to carry out through conventional

cost benefit analysis. Road user savings, a common means of measuring benefits, are negligible in these areas where very little traffic occurs. In addition even simplified methods of cost benefit analysis using the producer surplus approach may be inappropriate due to the lack ofgenerated surpluses[6]. Although several simpler methods of appraisal have been suggested none of these accommodate the need to often consider multiple objectives in the process of evaluation [7, 81.

The problem of investing m and selecting a prior- itized set of rural development projects such as roads may also be viewed as a decision process in a locational context[9]. There is now a sizeable literature in economics, geography, and regional science which examines various aspects of locating facilities efficiently. Recent research ranges from continued improvements m the mathematical solution of the problem by a variety of optimization methods to the consideration of models which recognize social equity goals as well as efficiency[lO, I I]. A relatively small portion of the literature, however, deals with location allocation analysis as applied to developing countries. As Fisher and Rushton have noted despite insufficient development budgets significant locational decisions are made daily in the poorest of countries[ 121. At a time when both the less developed nations themselves as well as donor agencies are concerned about rural poverty reduction it is important that more attention be drawn to developing methodologies and applying solutions to these critical needs. Often locational decisions with respect to rural facilities must be made in the absence of minimal access standards. Thus, in many respects, the more important problem and questlon is where should road improvements be located. Then given those allocation decisions the goal of locating other facilities and maximizing access to those facilities can be approached within the framework of integrated or complementary rural development.

THE PROBLEM CONTEXT

Indonesia, as many other developing countries, has

SEPS bol 17. No ,--A I

2 T R. LENBACH AND R G CROMLFY

been experimenting with solutions which aim at reduc- ing deep seated rural poverty. Over the past decade 1 IO million rural Indonesians have been the target of programs which aim at increased food production. employment opportunities, and equitable income distribution In 1974 a cash incentive Rural Works Pro- gram emerged as a major vehicle in these efforts. Essentially, the program provides immediate income supplements to those kecumutun (sub-districts) which were defined by BAPPENAS (National Planning Agency) as tniskin (poor), often well under the average per capita income of US $170 for all of rural In. donesia. Local residents paid a daily cash wage (Rps. 25g-350 or US 5Oc) in exchange for labor on infrastructure projects, usually simple roads or irrigation works, in the immediate area. Under a $25 million loan from the U.S. Agency for International Development, some 1480 projects will be constructed in about I 100 of the poorest kecatmtans under Repel- ita III, the National Devlopment Plan. Until recently the major concern of the program was to deliver m- come supplements to needy people quickly without regard to project impact. More recently, however, the U.S. Agency for International Development (AID) and the Government of Indonesia (GOI) have become concerned about quality, long run social and economic impact, as well as location. During 1978 and 1979 field work was carried out which attempted to detail impact and isolate critical criteria which would contribute to the success of the investments. A specific task, given a set of selection criteria. was subsequently to develop a project selective model which would allow a decision making umt to determine locations of the most beneficial projects from the larger set of potential projects [ 131.

The objective in the following sections is to present a general selection methodology which will have application to a wide variety of situations and circum- stances in the developmg world. The illustration provided, however, utilizes the Indonesian field experience and selection and impact criteria with respect to rural road improvements under the Rural Works Program of the GOI.

A METHODOLOGY FOR PROJECT SELECTION

The evaluation of alternative projects for public investment is one aspect of modern decison analysis. This planing process assumes that man or society has the ability to structure environmental conditions to produce desired consequences. The evaluative process requires then that the decision maker is able to iden- tify: (I) a set of alternative actions; (2) the relationship between each action and consequence; (3) some degree of preference for each consequence[ 141.

However, as discussed above. there are many problems associated with operationalizing any model designed to formulate public policy. First. the consequences of alternative actions are difficult to define and those that can be defined are usually expressed in different units or even scales of measurement. A frequent problem is how objectives expressed in qual- itative terms can be compared wth objectives expressed in quantitative terms. Attempts to convert various value measurements into a universal criterion such as utility or benefits have been largely un- successful.

Another major problem is that the decision making agency usually has no single goal or objective. Conflicting social interests make it extremely difficult to decide what goals should be achieved at the expense of others. Each project and its consequences are in competition with other projects and then consequences for scarce resources, Satisfying one objective often means that other goals must be reduced in scale or completely neglected. Therefore, the dilemma of the decision maker IS to achieve his set goals to the fullest extent possible in an environment of limited resources and conflicting interests The goal programming approach has been suggested by several authors as an effective solution methodology for ratio- nal decision making in such an environment[l5~ 171.

THE GOAL PROGRAMMING APPROACH

Goal programming is a management science technique that is an extension of linear programmmg[l8]. However, unlike its predecessor, a goal programming model does not have a single cost-minimizing or utility maximizing objective function and does not require a common unit of measurement for all variables. In- stead, goal programmmg only requires that each cari- able can be measured and a preference ranking for each objective can be established. Each objective in a goal programmmg formulation is represented by an inequality expressing a desired level of attainment The jth objective is assigned a corresponding deviational variable 4 that represents the reduced level of satisfaction associated with thejth objective.

To solve the problem of conflicting multiple goals, goal programming uses an ordmal hierarchy approach. Each of the N objectives are assigned to one of Kdifferent priority levels. Within each level, all objectives have the same priority; however, between groups. lower-order objectives are considered only after higher-order goals have been satisfied. Mathe- matically. this is accomplished by assigning a priority weight P, to each deviational variable of the Kth group. To insure the dominance of the higher-order group, P,, must be a magnitude greater than pk + , such that the product of a very large number 31 and P, . , can never be greater than or equal to P,.

The ordinal hierarchy approach iteratively reduces the feasibility set of potential solutions until either all goals are fulfilled or no feasible solution can be found; that is the deviational variable of one or more goals are included in the final solution vector. If no feasible solution can be found that satisfies all levels of objectives, then the fulfillment of lower-ordered goals will be solely determined by the last ranked group being considered when infeasibility occurred. For example, suppose that out of a set of 100.000 alternative actions only 1000 actions satisfy all P,, level goals. Of these 1000 actions only 100 satisfy both all P,, and P, level goals and only 7 of these 100 solutions also fulfill all P, level goals. However. none of these 7 alternatives satisfy all P, level goals. Therefore, the achievement of P, and all successively lower level goals are the by- product of the partial satisfaction of P? level objectives. In this instance, it is possible that some P, level goals may be fulfilled while none of the P, level goals are met.

Also, a weighting or trade-off factor 1% may be assigned to each deviational variable to determine how much a unit increase m one variable is necessary

A goal programming approach to public investment decisions 3

to offset a unit decrease in another variable of the same nique to a complex problem with multiple, often priority level. In general terms, the project selection conflicting, goals. The Indonesian governments in- model can be written then as the following goal pro- vestment in infrastructure improvements under the gramming problem: Rural Works Program is typical of the year to year

n dilemmas faced by development planners. Essentially,

Minimize 1 u;P,d,; (1) in the example, planners are forced to select locations ,=I m which to implement rural road projects. The deci-

Subject to 2 A,.u, + d, > b,V,; sion process is complicated by a variety of criteria that

(2) may be used to assess which projects will result in the ,=I maximum benefits. Moreover, the decision maker is

x,, d, > 0; (3) forced to consider a multiplicity of goals. This section describes those criteria that are critical to the selection

where M; is the weighting factor for thejth objective; P, of rural road projects. With these criteria as the base is the priority factor for the jth objective; d, is the for the model, goals are established, priorities as- deviational variable for thejth objective; A,, is the per signed, and solutions are derived for a problem setting unit consequence contribution of the i th project to in East Java (Fig. 1). thejth goal; X, is the support level for the ith project; As mentioned previously, the dual objectives of the and h, is the specified level of attainment for the ith Indonesian Rural Works projects are to inject cash objective. wages into very poor areas, quickly, and to enlarge

The objective function (eqn 1) insures the hier- development opportunities. Recently more im- archical satisfaction of the various goal levels. Equa- tion (2) specifies the desired attainment level for each

portance has been attached to the overall development impact which will accrue as a result of the

objective, while eqn (3) provides the nonnegativity construction of infrastructure projects. Detailed field requirements for all project and deviational variables. inspections and extensive discussions with village While a modified simplex algorithm has been devel- letters and local Department of Manpower officials in oped for solving any goal programming problem, the over 75 actual project areas in Java, Sumatra, Bali above model can be solved practically by any simplex and Sulawesi allowed the development of a list of code if the weighted priority factor w,P, for each devi- criteria which are critical to the success of road ational variable of the Kth group is greater than w,P,d, projects. The criteria are detailed in Table 1. for each objective of the K + 1 group. Initial variables selected for inclusion in the model

provide requirements which emphasize the number of people who will benefit. They stipulate that a min-

RURAL ROADS SELECTION CRITERIA imum number of people must be served per project The full utility of the goal programming approach is (VARIABLE 2) and that an aggregate, combined

best illustrated through the application of the tech- population threshold level across all projects selected

JAVA SEA

STRAITS OF MADLRA

0 Road Pro,ectr INDIAN OCEAN

Fig. 1. Locanons of potential rural road projects in East Java, Indonesia.

T. R. LEINBACH ANLI R. G. CROMLEY

Table 1. Road project selectlon variables

1.

2.

3.

4.

5.

6.

7.

8.

9.

10.

11.

12.

13.

14.

TOTAL POPULATION - aggregate number of people served by all road

projects selected for construction.

THRESHOLD POPULATION - requirement of minimum, critical popula-

tion serving a village or villages within a ten kilometer band on

either side of the proposed linkage.

TOTAL AGRICULTURAL LAND - amount of currently productive wet rice

or other agricultural type land to be served by the proposed road

plus the amount of potential agricultural land or that which may

be converted.

POTENTIAL AGRICULTURAL LAND - an addItIona variable created by

separating out only the amount of potential aqricultural land

from the actual cultivated land In #3 above.

RESOURCE CONVERSION REQUIREMENT - a measure of the requirement,

necessary within a specific project area, to convert, on the

average, one hectare of potential arable into actual cultivated

land. Measured In man/days (an average working day per individual),

the resources required vary and are a function of site, sltuatlon,

crops to be planted, etc.

HIGHER ORDER CONNECTION - measure of whether the proposed road

project links to a higher order road and, thus, serves to build

up a major exit-entrance portal.

INTERNAL ACCESS - extent to which the proposed road proJect builds

up Internal access in the district by connectinq up with existing

roads.

INTEGRATED DEVELOPMENT SCHEME - whether or not the proposed road

project is part of a 'package' of other improvements in the area

or whether a complementary development scheme is proposed for the

area.

DAILY MARKET DISTANCE - distance to the closest active daily mar-

ket; objective 1s to improve access in those districts which

already have a nearby market: road project is to make the market

more 'usable'.

FACILITIES SERVED - extent to which proposed road gives access to

schools, health care facilities, extension offices, etc.

ROAD LENGTH - if the road lenqth IS excessive (greater than eiqht

kilometers) it will be difficult to maintain.

BRIDGE COSTS - heavy bridge costs reduce funds which could be

allocated to construct other, additional projects Delays in

bridge construction often reduces the full use of the proJect

until bridges are completed.

ESTATE LAND_ - desire to minimize government owned estate land in

project area; projects are intended to serve rural poor: estate

road funds should be derived frwn alternative sources.

OFF SEASON UNEMPLOYMENT - projects should provide temporary waqe

income for unemployed; high rates of seasonal unemployment in-

dicate need.

A goal programming approach to public in~est~ent decisions

15.

16.

17.

18.

19.

Table 1. (Cord)

COMPETING ROAO - proposed project should not be built in an area

where there is a nearby or parallel facility which competes in

objectives.

DISTAIICE TO SURFACE MATERIALS_ - availability of gravel, stones, _l_l

etc. within a reasonable distance (under 30 kilometers) of the

proposed project site.

PROJECTS COST TOTAL - combined costs of all projects constructed _._.

must not exceed a critical limit.

SELF HELP LEVEL - projects should be located within areas where __~

there is and has been a strong record of intra-village cooperation

in accanplishing goals.

PROJECTS CONSTRUCTED - a minimum number of projects which must hi? -___--

constructed.

must, as well, be achieved (VARIABLE 1). In order to expand food production projects should serve land that is now agriculturally productive but will become more so with improved access. In addition, considerable weight will be given to those projects which serve potential agricultural land. These variables have been incorporated in the model so that emphasis may be placed upon total agricultual land (currently cultivated and potentially cultivable) (VARIABLE 3) and also upon potential land separately (VARIABLE 4). Recognition is made that some land will be more difficult to bring under cultivation as a result of differences in site, situation and the amount of labor needed to convert to a specific use. Converting raw land to wet rice production, for example, requires some terrace (sawah) construction and hydraulic engineering, whereas other crop requirements may be less demanding. Therefore, an average resource conversion estimate is incorporated for each project location.

A series of variables measures the impact of the completed project upon overall accessibility. Linking to a higher order road (VARIABLE 6), improvements to the circuitry and connectivity of the- local network as a result of the new construction (VARIABLE 7) and the location of the proposed road project so as to avoid strong competition and redundancy of network function (VARIABLE 15) are all important accessibility impact measures and partial predictiors of benfit growth after construction.

Several selection variables relate to specific construction requirements. First, it is desirable that the road project be limited in length. Of the sample projects surveyed it was learned that 5-8 km is a desireable length while longer projects are more difficult to organize and construct (VARIABLE 11). Especially critical is the finding that long road projects are much more difficult to maintain and, thus, it is highly likely that such projects will not receive the maintenance and upkeep essential for normal usage. It is also advantageous to have surfacing materials, gravel and stone, commonly available within a reasonable distance (VARIABLE 16). The

degree and history of village cooperation and self- help efforts (VARIABLE 18) are important indi- cators of the probable quality of construction and likelihood of continued maintenance and upkeep. Finally, extensive bridge requirements along the route are a negative factor (VARIABLE 12). Bridges require special expertise and skill levels which cannot be supplied by local labor. Even if designed by civil engineers, bridges must be constructed by other government agencies and often long deiays result. The construction delays mean simply that the benefits of the road will be diluted and delayed.

Another group of variables measures the likely impact of road construction with respect to broader development objectives. For example, if a proposed project was part of an integrated or complementary rural development scheme (VARIABLE 8), if it provided improved access to an existing, nearby market (VARIABLE 9) or if it served a variety of public facilities (markets, schools, small factories, health care and family planning posts) (VARIABLE lo), such a project would be more desireable than one which did not. A distinct objective of the program, in addition, is to improve the Ievel of living among the poorest groups. Consequently, a measure of the magnitude of seasonal unemployment (VARIABLE 14) provides an indication of where cash incentive funds should be dispersed along with a crude measure of the availability of labor for construction. The greater the extent to which the proposed road serves estate land (whether government or corporately owned) is viewed as a negative factor {VARIABLE 13). Benefits are to accrue to the rural poor and not the government or individual firms. If roads are needed to improve access to estate or plantation areas construction funds should be derived from alternative government or private sources.

Finally, it is often useful to specify a minimum number of projects to be built (VARIABLE 19) and that the combined costs of those projects will not exceed the amount of funds available (VARIABLE 17). In the first instance it is critical that we avoid achieving our requirements or goals with too few

6 T. R. LEINBACH AND R. G. CROMLEY

projects and, thus, insure some degree of regional equity and, of course, political satisfaction. In the latter instance, while it may be possible to squeeze out some small incremental funding for additional worthy projects in extremely needy areas or under emergency conditions (drought or flooding), the rupiah allocation to the Rural Works Program is fixed. Expenditures on a set of projects in any fiscal period, therefore, may not exceed the governmental commit- ment.

THE BASIC GOAL PROGRAMMING MODEL

Given the establishment of the above selection criteria, the next task is to define the attainment or goal level with respect to each criterion as well as the ranked hierarchy of goal achievement levels. For the basic model, the 19 criteria are aggregated into six

separate priority levels and assigned specific attainment levels (Table 2). The most important objectives are grouped together at the PO priority level; goals in the P, priority level are less important than those at P,, while goals with a P, ranking are considered to be least important in this model. Initially, the weighting factor for each variable is set equal to one so that a unit decrease m one variable will just offset a unit increase in another variable at the same priority level. This assumes that a planner cannot make any distinctions among variables and that all variables have a com- parable range of values. Subsequent versions of the basic model relax these assumptions by utilizing the weighting capability.

Because it is impossible to implement partial projects, the basic model and all variants were solved as a mixed-integer programming problem. In the ini-

Table 2. Attainment levels and goal priorities: basic model

Priority Level Criteria Attainment Level

pO

pO

pO

pO

pO

pO

p1

p2

p2

P3

p3

P3

P3

P4

P4

p4

p4

P5

p5

1.

11.

12.

14.

15.

17.

19.

2.

3.

8.

10.

16.

18.

4.

6.

7.

9.

5.

13.

Minimum Total Population Served

Maximum Road Length Per Project

Maximun Total Bridqe Costs

Minimum Off Season Unemployment

Level Per Project

55,000 people

8 kms

30 million rupiah

Maximum Number of Carpeting Roads

Maximum Total Project Costs

Minimum Number of Projects Funded

Threshold Population Served Per Project

Mlnlmum Total Agricultural Land Served

Minimum Number of Projects that are

Part of an Integrated Development

Scheme

35 percent unemployed

3 roads

260 million rupiah

12 projects

2,000 people

2,250 hectares

Minimum Number of Facilities Served

Maximum Distance to Surfacinq

Materials Per Project

Mlnimum Percentage of Villages that

have Demonstrated Adequate Self-help

Records Per Project

Minimum Amount of Potential Agricul-

tural Land to be Converted

Minimum Number of Projects that Link

to a Higher Order Road

Minimum Number of Projects that

Improve Internal Accessibility

Maximum Distance to a Daily Market

Per Project

1 project

15 facilities

30 kms

50 percent

650 hectares

5 projects

7 projects

12 kms

Maximum Man Days Avallable for Land

Conversion 200 man days

Maximtur Amount of Estate Land

Included Within Project Areas 100 hectares

tml solutton. 13 of the 20 potential East Javan road projects are chosen for implementation (Fig. 2). All projects in the easternmost areas of the province were selected for possible constructton based primarily upon the large population, high rates of un- employnent and low overall bridge costs in the region. Addrtronally. another cluster of projects in the north- cm portion of the provmce were selected given the criteria levels assrgned to these variables.

In the basic model, all of the PO goals are satisfied except bridge cost ( # 13) and road length ( # 11) (see Tahfe 3). the reqmrement levels of these two goals are m confltct wtth the attainment levels of other goals at the P,, level and could not be satisfied. Since no solutmn set could fully achive all PO requirements. the achievement of subsidiary goals occurs, basically. as a srde effect. The fact that some PJ goals are fulfilled whrle no f2 goals are met IS a function of the highest prmrtty level that was achieved, in this case the P, requirement. rather than an explicit consideration of these lower-ordered objectives

IARIATIONS OF THE BASIC MODEL

The second application examines the impact of changes in the priority assignment of individual goals Recall that in the first solution there was a confhct between road length and bridge costs ris-a-ris other goals within priority level P,. In the current model. the total population goal ( # 1) 1s lowered to the P, level to determine the impact upon project selectron. Having adjusted the population priority level, the new solution reveals only 11 proJects which are now selected for potential construction (Fig. 3). The projects at Gemarang ( # 4) and Tikung ( # 17)

are omitted because the former has relatively high bridge costs and the latter exceeds the maximum road length. This outcome was suppressed in the previous run because both of these projects had very high populatton levels. Clearly, the satisfactton of the population goal conflicted wrth the road length and bridge cost goals as these latter objectives are now satrsfied whtle the former is not. The only other distinction between the first and second solution is that the goal associated with the number of projects to be implemented ( # 18) is not achieved in the latter run.

Although all PO goals are now ful~lled. no solution exists that satisfies this requirement and any P, objective (Table 3). The solution that was derived does, however. minimize to the furthest possible extent. the deviational variable associated with the total population goal. Again, the fulfillment of a subsidiary goal 1s merely a residual effect of trying to satisfy goals at a higher level, in this case the P, level.

In the first two models, it was assumed that a planner IS unable to make any distinction among objectives in the same priortty level. However, as discussed above, It IS possible to discriminate among objectives within a priority level by using a weighting scheme. This approach alters the trade-off designa- tion among goals. That IS. m the previous solutions, one unit of bridge costs was equivalent to one person which was equivalent to one percent off season unemployment. In such a system, more emphasis is naturally placed on those goals whose assoctated deviational variable has a greater range of values. Thus, m the basic model the achievement of the road length and bridge costs goals was at a disadvantage when compared against total populatton. Suppose.

\-i--, JAVA SEA CENTRAL JAVA ; / I

EAST JAVA

STRAITS OF MALXIRA

0 Project In Solution 0 Project not tn Solution

0 25 50 75 SOLUTION ONE

KM

Fig 2. Solutron one.

m

Table

3.

Goal

ach

ieve

ment r

esu

lts

Number

Base

Model

Second

Model

Third Model

Priority

Weighting

Not

Priority

Weighting

Not

Priority

Weighting

Not

Level

Factor

Satisfied

Satisfied

Level

Factor

Satisfied

Satisfied

Level

Factor

Satisfied

Satisfied

1

11

12

14

15

17

19 2 3 a

10

16

ia

4

6

Minimun

Total Population

Served

PO

Maximum

Road Length Per Project

PO

Maximum

Total Bridge

Costs

pO

Minimum

Off Season

Unemployment

Level Per Project

pO

Maximum

Number

of Competing

Roads

PO

Maximum

Total Project

Costs

pO

Hinimun

# of Projects

Funded

p1

Threshold

Population

Served

Per

Project

p2

Minimtan Total Agricultural

Land

Served

p2

1

x

p1

x

pO

x

pO

pO

pO

pO

P 1

x

p2

x

p2

x pC

pO pn

pO

pO

pO

x p1

x

pz

x p2

1

x

1

x

50

x

1

x

1

x

1

x

1

x

1

x

1

x

1

x

1

x

x

Minimum

Number

of Projects

that

are Part of an Inteorated

P_

1

x

P3

p3

x

p3

p3

p3

P3

1

x

1

x

x x

Development

Scheme

_

3

Minimull # of Facilities

Served

P3

Maximun

Distance

to Surfacing

Materials

p3

Minimm

% of Vlllaqes

that have

Demonstrated

Adequate

Self-Help

P3

Records

Per Project

1

x

1

x P3

p3

Minimum

Amount

of Potential

Ag-

ricultural

Land to be Converted

p4

x p4

p4

p4

p4

p4

Minimum

# of Projects

that Link

to a Higher

Order Road

p4

Minimun

Number of Projects

that

Improve

Internal

Accessibility

p4

Maximum

Distance

to a Daily

Market

Per Project

p4

Maximun

Man Days Available

for

land Conversion

p5

Maximum

Amount of Estate

Land

n

1

x

1

x

x 1

x

1

x

1

x

1

x

x

P 4

x PA

x

p4

1

x

p5

p5

x

p5

1

x p5

Included

Within

Project

Areas

'5

A goal programming approach to public investment decisions

JAVA SEA

0 Prolect in Solution 0 Prqect not in Solutton

cl 2F 50 79 SOLUTION TWO KM

Fig. 3. Solution two.

JAVA SEA

CENTRAL JAVA j

STRAITS OF M4DCR4

0 Protect In Solution

0 Proleft not rn Solutmn

0 25 50 75 SOLUTION THREE KM

Fig. 4. Solution three

SEPS Vol 17. No I-B

10 T. R. LEINBACH AND R. G. CKOMLEY

now, that a planner decides that one unit of bridge costs is equal to 50 units of all other variables in the PO prionty level of the basic model. The planner, in effect, has altered the rate of substitution with respect to the entry and exit conditions of deviational variables within this common priority level.

This scenario was used to develop the final model. In the final solution, 13 projects are again selected for potential implementation (Fig. 4). The distribution of selected projects is similar to the base run. All of the easternmost projects within the East Java province are again selected. The changes occur in the northern part of the province where Ngoro ( # 1) and Balerejo ( # 3) are substituted for Gemarang ( # 4) and Plan- daan ( # 15). This substitution results from the heav- ier weighting of bridge costs when compared with off season unemployment rates. In this solution bridge costs and total population are satisfied while off season unemployment and road length are not (Table 3).

CONCLUSIONS

Development planners in Third World areas are forced to make frequent decisions involving the allo- ciation of large sums of government funds. Often these decisions involve conflicting goals, and a major problem is to decide which goals(s) should be satisfied at the expense of others. The allocation of rural road projects viewed within the two maJor goals of imme- dite income supplements to very poor areas and the improvement of the development potential of such areas is one such situation. Once appropriate criteria related to project success have been isolated and measured, goal programming provides a useful approach to the complex problem of allocating road projects to sub-disticts.

An important feature of the goal programming approach is its generality and flexibility. The illustration provided in this paper views only the assignment of road projects in the rural development effort. Clearly, the problem could be expanded to included additional infrastructure projects such as irrigation canals, markets, and other facilities which have a reat potential role in income improvement. In addition, a wider variety of selection criteria may be used. For example. it may be useful to provide criteria which limit the assignment of proJects to specific areas in order that an equitable spatial distribution of public investment results.

As with other normative models, goal programming may also be used in sensitivity analyses. For example the decision maker might select initial attainment levels and/or weights for each of the selection criteria. Subsequently, it might be useful to adjust these levels downward or upward to determine the impact upon goal satisfaction and the spatial allocation pattern of development expenditures. As an illustration the planner might simulate the impact of a sharp budget cut or the impact of a construction surge in integrated development projects. Clearly the senstivity analyses may incorporate adjustments to the goal priority levels as well.

Along with these clear advantages, several drawbacks must be cited. First, it is clear from the illustration provided here that it is necessary to provide a quantitative measurement for all project selection variables. The accuracy of these measurements that determine impact are critical to the ultimate decision

making process and its solutions. In addition, the choice of appropriate and efficient criteria assume that the consequence of such criteria has been assessed. Secondly, the level of sophistication of goal programming methodology means that the application of the tool is limited to analysis at a central decision making unit. Simpler selection models may be utilized in the field at the local level to obtain those projects which will be submitted to the Rural Works Program and BAPPENAS in Jakarta for consideration in the final construction phase.

Finally, the availability of goal programming source codes is still limited. This drawback will be less restrictive m the future, however, as more researchers use goal programming to solve problems in a complex environment.

AcknowIedgement.c~Professor Lembach acknowledges support from the Natlonal Science Foundation, INT-77-07325 and the University of Kentucky Research Foundation Graphics were prepared by the Umversity of Kentucky. Department of Geographys Cartographic Laboratory

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