Application of a computer-aided expert decision support system to rural development in Kenya

Cornput., Environ. and Urban Systems, Vol. 16, pp. 41 S-433,1992 Printed in the USA. All rights reserved.

0198-9715192 $5.00 + .OO Copyright 0 1992 Pergamon Press Ltd.

APPLICATION OF A COMPUTER-AIDED EXPERT DECISION SUPPORT SYSTEM TO RURAL

DEVELOPMENT IN KENYA

Deborah Fields

Office of Strategic ~~it~a~jves, U.S. Army Corps of E~g~~e~rs

T: John Kim

ABSTRACT. This paper Ascribes the utility of a computer-aided system that couples an expert system

with other planning modeling techniques and hypermedia as a method to support institutional development. Using an example of rural development in Kenya, the paper describes the application of a computer-aided expert decision support system to aid local planners and administrators plan for investment in infrastructure to selected rural service centers.

INTRODUCTION

Advances in computers make computer-assisted instruction (CAT) a powerful method to improve the functions of management development in training, consulting, and research. Steinberg (1991) describes CAI as a “synthesis of technology, theory, and practice.” Cornputer- aided systems provide tools for learning. Video-taped playing, computer-based games, and interactive simulations are now being used in many developing countries. Computer simulation allows users to make decisions and observe their consequences.

This paper describes the utility of a computer-aided system that couples a numeric index, a general economic forecasting model, a database function with an expert system, and enhanced user-interface as a vehicle to capture the dynamics of rural development (including the importance of political and human factors, the unce~nties, the shortages of data and skilled man- power), and to deliver that information to support local planners and administrators plan infrastructure projects in rural service centers of Kenya. The integrated system provides a mecha- nism for representing a set of qualitative and quantitative information about rural development

Reprint requests should be senl to Dr. Deborah Fields, Community Planner, Office of Strategic Initiatives, U.S. Army Corps of Engineers, Washington, DC.

415

416 D. Fields and 7: J. Kim

which can be used by local planners and administrators to: (a) identify rural towns with the most potential to serve the functions of rural service centers; (b) select alternative infrastructure investment packages; and (c) evaluate the feasibility and consequences of alternative infrastructure investment packages on the growth and development of rurdl service centers.

The paper is divided into four parts. As background on the complexities of rural development and the potential benefits that computer-aided expert decision support systems offer, the fast section of the paper is an introduction to the rural development problem in Kenya that is also common to most other developing countries. The second section of the paper centers around the potential usefulness of a computer-aided framework that integrates expert systems with other planning modeling techniques as a vehicle that supports the delivery of information to planners and administrators, and the acquisition of knowledge from that information useful to decision making. The third section of the paper describes the development of the proposed integrated framework to support planners and administrators responsible for rural development in Kenya. Finally, the last section describes the initial findings from the application of the proposed expert decision support system to the Rural Trade and Promotion Centers (RTPC) program.

RURAL TRADE AND PROMOTION CENTER PROGRAM IN KENYA

Kenya, like many other African countries, has adopted comprehensive rural development strategies that promote rural-urban linkages and strengthen the lowest level of the urban hierarchy. Under its Rural Trade and Promotion Centers (RTPC) program, put forth in Sessional Paper No. 1 of 1986 (Government of Kenya, 1986), the Government of Kenya is to concentrate development resources in selected small urban centers to provide a range of basic physical infrastructure and facilities to support agriculture and other productive, employment-generating activities. These investments are expected to enhance productivity and relieve bottlenecks hin- dering the interactions between the small urban centers and the hinderl~d, and thus stimulate regional income and growth. Such bottlenecks may include poor farm-to-market roads, a lack of electricity or water, or inadequate facilities for the collection and marketing of farm products.

To date, eight market centers have been chosen for the first round of investment and another round of selection is being prepared. Examples of proposed RTPC projects include open air markets, water provisions, bridges, town road upgrading, storm water drainage, and bus parks. An estimated Ksh 15 million (roughly equiv~ent to $850,0~ assu~ng an exchange rate of U.S. $1 to Ksh 23) was set as the ceiling for any given package; however, most of the first eight RTPCs were priced at around Ksh 20 million.

The implementation progress to date for the first round selection of RTPCs has not been successful. A number of studies including Evans (1986, October), Smoke (1989, July; 1989, September), Onyango (1989, November) and Smoke and Wheeler (1990, March) have pointed out that political realities interfered with the implemen~tion of the program. As a result, the criteria developed to select RTPCs in each district need adjustment to reflect the realities of and theories behind market town development and the role of infrastructure. One study in particular by Smoke and Wheeler (1990, March) concludes that there is legitimate concern over whether the investments made under the RTPC program meet the goals of the rural-urban balance strategy. They explain that lack of current knowledge of local circumstances and rural urban economic linkages do not permit a confident selection of inte~entions that will s~eng~en those linkages. As a result, the packages selected in the first round of RTPCs comprise only public service infrastructure, which, while being useful, may not tackle the most significant bottlenecks constraining development of the centers and their hinderland. Macro-economic policy, non- physical interventions to support trader organizations, or training may all provide greater stimu- lation to economic activity in the RTPC and its hinderland. Neither the Ministry of Planning

Applying CA1 to Rural Development in Kenya 417

and National Development (MPND) nor the districts have been able to provide the managerial or technical expertise required to plan and implement the RTPC program. The project managers admit that some delays could have been avoided by better planning and coordination.

The causes of under-development are complex and require planning, coordination, and solu- tions that take this complexity into account. Information available to decision making must support understanding of this complexity. This is not to imply that planners must know every- thing before design, org~i~tion, and implementation of a project. Rather, as Johnston and Clark (1982) contend, it is important that planners accommodate uncertainty, comprehend a few more interactions, and avoid truly disastrous and irreversible mistakes through better analysis and knowledge. Identifying alternative courses of action for rural development demands an analysis that focuses on the objectives of the government policies and the specific conditions and requirements of the regions under study. The following section describes the role of computer-aided systems as a vehicle to deliver information to support local planners and administrators responsible for rural development projects, and support them in obtaining knowledge that is useful to perform their planing functions, and to improve decision making.

A COMPUTER-AIDED EXPERT DECISION SUPPORT SYSTEM FOR RTPC PLANNING

To assist rural development planning, a computer-aided expert decision support system is developed. The system is designed to improve the flow of information available to decision making for rural development projects. Specifically, the system captures information of the fund~ent~ elements of rural service centers and transfers this information to planners and administrators of the RTPC program in a manner that provides them with a process for focus- ing analysis and for thinking through project planning. As such, the system is designed to bridge the gap between planning and execution.

The framework for the computer-aided expert decision system for the RTPC program is depicted in Figure 1. Each component of the system is explained in detail. The system is designed to operate in a Macintosh environment with a minimum configuration of IIci. The system uses SuperCard by Silcon Beach Software version 1.5 (1989-1990) to supply the enhanced user-interface; Nexpert Object, a development expert system shell by Neuron Data version 2.OB (1991), to fill the role of the inference engine and knowledge base, and Microsoft Excel by Microsoft version 2.2a (1989) for the database management function and model base, including the numeric index and the general economic foresting model.

The system is a prototype to demonstrate its utility to aid local planners and administrators plan for investment in infrastructure to selected rural service centers. It is developed using data from six RTPC candidate towns in the Kirinyaga District of Kenya. The Kirinyaga District makes a useful case study since there are available economic models and studies of the Kutus market area from previous research on the RTPC program. Kutus was selected as the RTPC for the District during the first round of investments. Currently a new bridge, an upgrade of a new market facility, water drainage for the market, and market roads and water drainage for the roads are being constructed in Kutus using RTPC investment funds.

The User Interface

The user interface, as shown in Figures 2,3, and 4, is the most important feature of any project planning tool if it is to be utilized by planners and administrators of developing countries. The interface has three primary information flows: (a) guidance for data collection and analysis; (b) information to support identi~cation of alternative infras~ucture project packages for

418 D. Fields and T: J. Kim

Knowledge

User - Inference Interface 4 Base

FIGURE 1. Decision Support System for the RTPC Program Using Expert Systems Technology.

investment; and (c) infor~tion and reasoning about the impacts of ~temative infras~ct~e project packages on the ability of the market town to serve the functions of RTPC.

Figure 2 represents the table of contents for the system. Users can access any component of the system from this screen. Upon entry into the system, users are provided with guidance for data collection from each candidate RTPC in a district. The guidance provides a description of each type of data needed, why it is required, and how it can be collected. Once collected, the interface queries users for that data, then the interface sends the data to the inference engine, which in turn sends the data to the model base for c~culation of the RTPC index (discussed in more detail below) and storage in the database (also discussed below). After the numeric index has been calculated for each candidate RTPC, the towns are ranked according to their score. Then the results are gathered by the inference engine which sends them to the knowledge base where the infrastructure gap is identified for the town determined to have the most potential to fulfill the functions of a rural service town (discussed in more detail below). The inference engine then reports this information back to the user through the interface.

At this point the user can query the system to explain the results of the ranking and the identified infr~~ct~e gap. Given the RTPC budget constant, the user then selects a package of infras~uct~e projects for investment in the selected RTPC from a list provided by the system. Dollar estimates for each type of infrastructure are developed from the costs reported to support infrastructure investments in the RTPC program to date and from other sources.* UseiS can augment these estimates through the interface. Figure 3 presents the interface that transfers this information to the user.

The inference engine then sends the user’s inputs to the model base and the knowledge base. The total cost of the infrastructure investment is input into the general economic forecasting model based on a social accounting matrix (SAM), and the model is recalculated producing an estimation of the 5-year economic impact from the investment. The specific details of the general economic forecasting model are discussed later in the section that describes the model base functions. At the same time, the knowledge base using the stored decision rules calculates the impacts from the package of infrastructure investments on the functions of the rural service towns. These decision rules are discussed in more detail in the section which describes the fea- tures of the knowledge base.

Applying CA1 to Rural Development in Kenya

- Table of Content

E F

click 0” any ropic

iil~ RTPC Q-it eria and Data Collection :$,:$::q~, !@# RTPC Town Database

j@%j!j; s&2 Calculate RTPC Index and Rank Candidate Towns

~~~~ Identify Infrastructure Gap and ~‘~+“.~~.~~~~:~ Prepare RTPC Investment Package

:~~~~ Results of SAM and RTPC Simulation Impacts

FIGURE 2. The User Interface.

The results from the model base and the knowledge base are sent back to the inference engine and reported to the user. Figure 4 depicts the interface from which users receive this information. The results of the economic impacts are presented graphically, while the result of

- KUTUS Kirinyaga

~~~,RTPC Design Infrastructure Investment Package

Ag. business training for Farm As. Ag. technology training for Farm As. Training for Ag Extension Service Organize members of Farm Association Bridge Rural Roads Market Upgrade Water drainage for Market Street Lighting Rural Electricity Extension Piped Water to Rural Areas Pumps for Water to Rural Areas Grain Storage Facility Amendments to Land Zoning Government Development Training Police Station Government Health and Environment Training

@j + component you wont to :s incorporate Into the package for ;* / ., ., I _.xA

Clear

FIGURE 3. Project Preparation - User Interface.

420 0. Fields and 7: J. Kim

RTPC tnvestment Impacts- K

RTPC Ir---’ ’ &xWw~l Imprm : i.... . . . . . . . . . . <,s . . . . . . . . . . . . \ . . . . . . . . . . . . . . s,. . . . . . . A..~ . . . . . . . . . ..A . . . . . . . ..I...~~...............,.....,..,,.....Y_*Y ..A .w “ydb’ $ * The lack of an organized farm association to 1

i; market produce to other markets constrains i: potential growth in the agriculture sector. $ l The RTPC investment - grain storage facility :~.:ina~aser..the,pot~al:..f~.~~.~ . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ..~.... 1..,14..,1,~,~.1...~? ,......

FIGURE 4. Feasibility Analysis Results - User Interface.

the impacts on the functions of the rural service center are reported in textual format. The graphic function uses interactive graphic capabilities of SuperCard. Both results appear on the same screen to enhance the users visualization. The results can be saved and printed. The user can reselect a package of infrastructure investments, change assumptions, and restart the system to estimate a new set of impacts from alternative infrastructure investment packages. The three information flows are depicted graphically in Figure 5.

Inference ~ffgiff~

As described above, the inference engine acts as the brain of the project planning tool. It con- tains the general problem-soiving knowledge (Waterman, 1986). It sends instructional messages

Results of I 1 Impact Analysis

in Qualitative and Quantitative Form

Data and Users’ Input

A i

Data

); Data Base

1 I Queries and f

Additional Da

Data n Retrieval

FIGURE 5. information Flow.

to the other applications, For example, it is responsible for sending data to the database for storage, and to the model base and knowledge base with directions for computation. Once computation is complete, the inference engine also retrieves the results and sends it to the interface.

Nexpert Object supplies the inference engine for the prototype project planning tool. Nexpert Object is a development tool and provides bi-directional chaining (forward and backward), opportunistic reasoning, and non-monotonic reasoning for pro~a~ng erections to the knowledge base. Nexpert Object also provides an open architecture for integration with egternal programs such as Microsoft Excel and SuperCard. The specific structure of the inference engine is defined based on the nature of the RTPC project planning needs. The computer- aided system for the RTPC program takes advantage of backward chaining to operate the decision rules contained in the ~owled~e base.

The database of the decision support system for project planning serves the function of data storage. Data are stored on each candidate KI’PC town by District. In the prototype system, data stored in the database includes those for six towns in the Kirinyaga District. These include Kutus, Kagumo, Kianyaga, Kiamatugu, Wanguru, and Sagana.

The data stored in the database represents a set of qualitative proxies cf the elements or characteristics in rural service centers. These proxies are developed based on theory of rural service center functions to meet the demands of the lowest level of the urban hierarchy and are explained in detail later in this section. Although there is no colon blueprint that applies to all rural service centers, it is possible to develop a multiple production function for a region that starts from theory and that is tended based on the specific ch~acte~stics and condi~ons of rural towns in the region to serve as a rural service town.2

Once users complete data entry for each town through the interface, the data are written to a file which is transferred by the inference engine to Microsoft Excel Spreadsheet for storage and manipulation. Users can access the database for each of these towns. Table 1 depicts the data stored in the da~base for each town.

The database also is responsible for storing the assumptions for both the expert decision rules and the general economic forecas~ng model. Users can access and change these assumptions. This capability allows users to explore the feasibility of alternatitle itlfr~~ct~e investment packages under various scenarios.

The model base serves two distinct functions in the prototype computer-aided system for RTPC project piannmg. First, it is responsible for calculating the RTFC index for the rural towns with potential to serve as the rural service center based on the data stored in the database. Second, the model base is responsible for calculating the economic impacts from alternative infrastructure investment packages on the selected RTPC town over a 5-year period based on a general economic forecasting model.

The RTPC Index The set of i~for~t~on about the f~nc~ons of rural service centers provides a basis for deter-

mining what actions can be taken in a particular region to transform an existing rural town into a rural service center. To accomplish this task, the first decision that must be made is to identify a candidate rural town with a region with the most potential to fulfill the functions of a rural service center. During implemen~tion of the first round of RTFC projects, rural towns were


TABLE 1. Database Input for Kirinyaga District

Pop. estimaZe of Market Ares

Per Capita Value of Optimal Yield

Cash Crop

KUTUS SAGAKA 48474 3(1818 16917 12079

1 0

Horticulture 1

Major Trunk Roads 3

% Tarmac 1

Number of Jua Kali 75

Numer of Servrces 126 Number of SSEs 2

Number of Gov’r Owned Business 2 Division Headquariers No

Ave. MD Suppliers Population 670.2

Ave. MD D&as Opened 0 442391 ConsVuclion Activity 22 Development Plans for I~XR-R9 76

Ave MDTrafftc Calmr 43 2

Market Area in meters :‘a

Rural Roads an Markcl Area 95

RailWay NO

Grain Storage No

Lwestock hucuo” Yes

Council Regulated Yes

Electrical Serwce in Urban Arca Yes

Electrical Supply (I” Kva) 1105

Number of Transformers 14

Street Lighiing No

Electric Supply in Kurai Area No

Substation Yes

Slaoghtahouser Yes

Council Operated Yes

Market Area Tamnac No

Market wilh Covered Stalls No

Market Council Regulated Yes

Drainage Around Market No

Market Roads ‘l‘armaced NO

IXainage Around Market Hoadr No

#of Financial lnst&“tcs ,

Days Gpened 2

Sewage Syswn No

Pipcd Water in Town “es

Access to River Yes

Access to Water IGltfatio” Yes

P,ped Wax ,n Rural No

Bus Park Yes

Bus Park Tarmac Yes Council Regulated Yes

Post Officx? Yes #of phone BOXCS 2

Automatic Dial No

Waste Disposal Facllily ‘Yes

Primary Schools Yes

Secondary Schools Yes

Po!ytcch”ics Yes

Vertxinary Facilities No

Mcdical and ffealth Clinics Yes

Police NO

#of Plols Available in Tow” 110 Ag Potential Around Town No

Budget Hislory (Deficll or Surplus) Surplus

Own Town Council Yes Judgemcnt on Developmet~l AIL. P00r

Most Receni Dcvclopmcnt Plan ,965

0

3

1

77

325 3

6

NO 326 4

O.l62222

53 123 11 4

38

83

YeS

No No

NO

Yes ,945

10

No

No

Yes YeS

Yes

No No

Yes No NO

No

t

2

NO

Yes

Yes

Yes

NO

Yes

No NO

Yes

3 Yes

Yes Yes

Yes

No

No

Yes

Yes

74 No

SJlPlUS

NO Poor

1982

KAGUMO WANYAGA 61407 27068

19425 28872

2 1

0 1

1

77 138

1

2 NO

452.8

0.327083

6 86

26.6 144

94

NO

NO

NC!

NO

Yes

200

8

No

No

No

Yes No

No No

Ye* NO

NO No

0

0

NC

Yor;

Yes

Yes

No

Yes

No No

Yes I

No

Y?S

Yes Yes

No

No

Yes

NO

1 Yes

Surplus

NO P00r

1962

0

2

05

45

74 0

2 Yes

58.4

0 283019

91 101 11

96

52

NO

No

NO

No

Yes

190

6

No

No

Yes Ye*

Yes

No No

Yes

NO N*

No

1 2

NO

Yes

Yes

Yes

NO

Yes

No No

Yes I

Ye5

Yes

Yes Yes

Yes

Yes

Yes

Yes

34 Y+?S

S”@“S

NO Poor

1978

KIAMATUGU WAXGL‘KU

selected for each of the recommended districts using a multiple index that included size of market area; agricultural and livestock productivity of the market area; agriculture potential of the market area; proximity of competing market centers; current level of infrastructure in the market center and its market area; and current level of economic activity in the market center and

its market area. The prototype computer-aided system uses a RTPC index that is based on this original index but includes the r~co~endations of studies evaluating its usefulness? relevant theory on ran-~ban linkages, and a survey of the region.

The RTPC index is calculated for each RTPC candidate based on the rmmeric representation developed to represent the multiple production function of rural service centers in the region under study. The function assumes the Urban Function for Rural Development (UFRD) approach by Rondinelli (1985) and is composed of nine elements. These include the market area size/population served; the agriculture potential and productivity; the location to the urban hierarchy; the economic activity; adequate supply of infrastructure; available land for development; the government administration; available social infrastructure; level of entrepreneurial spirit and business skills, and business climate.

The model base is secondly responsible for calculating the economic impact from the infrast~~ture investment in the selected RTPC town. To capture the important direct and indirect economic impacts of the infrastructure investment on the selected RTPC town region, a social accounting matrix (SAM) developed by Lewis (1989) with data from a survey carried out by Settlement and Resource System Analysis (SARSA)4 for the Kutus region is augmented to represent a 5-year general economic forecasting model. The Kutus region includes the market town and its hinterland. Planners and administrators of the RTPC program can benefit from the information provided by a general economic forecasting model to assist them in deter~ning which package of infrast~cture projects will provide the greatest economic benefits in the selected RTPC, in particular by type of household, productive activity, and institutions.

The Kutus general economic forecasting model is broken into production activities (farm, rural non-farm, town, coffee, food crops, livestock, farm-based non-farm activities, coffee pro- cessing m~ufact~e, retail, transport, services, government services, housing, and tinance), factors of production (land, capital services, hired labor. and family labor), institu~ons (households, companies, and governments, combined capital (capital account, harambee capital, and human capital) and rest of world accounts (activities, hired labor, and other). Harambee capital is a special investment fund collected by the government. The three capital accounts determine the available investment in the region. Most investment goods purchased inside the region are from the retail sector. Harambee investment is assumed to go to housing services, and human capital is assumed to go to government services for education. The rest of the world account accounts serve to collect imports and exports of commodities, hired labor, and other factor income moving into and out of the region.

The model assumes that factors of production, households, government and company institutions, production activities, capital accounts, and the rest of the world (imports) are determined by the model, while rest of the world (exports) and the RTPC project investment are given exogenously. Changes and injections in the exogenous variables determine the incomes of factors of production, househoIds, government, companies, production activities, capital account, and rest of world (imports).

It is assumed that food crop exports will increase by 2% a year over the S-year model period. This assumption seems approp~ate given the influx of wholesale buyers into the Kutus market ta purchase tomatoes. Data collected on the market shows that government receipts of taxes collected from market suppliers have grown approximately 2% each year over the past 4 years.

The RTPC project investment is added as a new column the SAM-based forecasting model. It is assumed that the investment decision is made in year 1 and that RTPC funds will be spent for cons~~tion in year 2, 3, and 4. The RTPC investment funds are split equally over the

0. Fields and T; J. Kim

3-year period, although users can change this assumption. The RTPC project investment is assumed to require skilled labor, retail and manufactured goods, government services, imported labor and imported retail, and m~ufact~ed goods. The coefficients for the RTPC investment project are assumed and specified in the SAM as retail S, m~ufact~e .2, imports .4, hired labor -2, and government .l. These coefficients assume that 10% of the RTPC investment income is spent on retail goods from Kutus town, 20% spent on manufacturing goods, 40% is spent on imported goods (including labor), 20% is spent on labor from the Kutus region, and 10% is for government services. These coefficients are based on examples of existing RTPC project cost breakdowns and a survey of the construction company managers responsible for construction of the RTPC projects in Kutus. A more realistic breakdown should account for coefficients that are project-specific. For instance, in the case of an electric transformer, it is likely that the RTPC project income will not be distributed to retail, manufacture, hired labor, or government. Rather, it will be spent on imported labor and materials. User can change these coefficients.

It is assumed that the RTPC investment will increase demand of family and hired labor on the manufacture sector by .05%, on the tr~spo~ation sector by .02%, and on food crops by .OS%. This assumption represents the impacts that the RTPC investment will have on access to the market area by buyers and sellers in the region. In addition, it is assumed that the RTPC investment will result in increase demand for government services by .05% each year starting in year 2.5 This assumes that government revenues will increase from the RTPC project through higher taxes and an expanded tax base.

The general economic forecasting model is represented in spreadsheet format with the columns of expenditures for each sector, rows of income, and their respective totals. Column totals are set equal to row totals. Then, average propensity coefficients are used to generate the fust round effect on income from induced changes in the exogenous account for the RTPC project. As total income changes, a new round of change is generated as total expenditures also changes, giving rise to additions demands for labor and goods from the region. This additions demand sets in motion another round of impacts referred to as indirect impacts. This iterative procedure produces the total impacts (direct plus indirect) on income in the region from the induced change. The model is set so that it will keep iterating until total change in income is less than BOO1 or where excess demand is zero. This iteration process is based on the Newton- Raphson method solution algorithm which has quadratic convergence properties (Dervis, De Melo, & Robinson, 1982).

Gross Regional Product (GRP) is measured from the model’s outputs as:

GRP = Consumption + Investment + Government + (Export - Imports)

where, Consumption is the sum of institutions (excluding Gove~ment), and productive activities consumption of goods and services; Investment is the sum of investment in the capital account, harambee account, and human account; Government is the sum of spending by the government sector; Exports is equal to the sum of exports out of the region, and Imports is the sum of total imports of goods and services into the region.

Once the total RTPC investment costs and coefficients are passed to the model base from the inference engine, the model is recalculated for each year to reflect the exogenous injection from the RTPC investment. The results of the 5-year general economic forecasting model are tracked in terms of the changes in household incomes (large and small farm households), manufacture income, trade balance (exports - imports), and total gross regional product. These results are sent to the inference engine which in turn passes them to the interface. The results are presented to the users graphically and numerically through the interface depicted in Figure 4.

Applying CA! to Rural ~evelop~enf in Kenya 425

Knowledge Base

The knowledge base is the fmal component of the decision support system. The knowledge base represents a collection of knowledge or expertise (Waterman, 1986). Two sets of knowledge are represented in the prototype system: a) rules to calculate the in~as~ct~e gap, and b) the functions of rural service centers in Kenya not captured in the model base and their relationships with the RTPC infrastructure investment. These rules allow for all relevant elements to be considered before a course of action is chosen.

Infrastructure ‘*Gap” Decision Roles The goal of the first set of decision rules is to identify the infrastructure needs (gap) of the

selected RTPC candidate. The infrastructure gap is simply defined as those economic and social infras~c~e components defined in the RTPC index that are missing in the selected RTPC town. The use of expert decision rules to identify the infr~~ct~e gap is p~cul~ly useful because of the qualitative and descriptive nature of the many of the rural service center functions; the relationships which exist among the economic and social components of infrastructure with the other functions of rural service towns; and the numerous uncertainties in the available data.

The knowledge base generates a list of the elements of infrastructure that are important to the function of Kutus as a rural service center. The list is generated based on the existing levels of physical infrastructure and the other functions and assumptions of the users. For instance, if access to the market town is blocked by a river, a bridge is identified as part of the in~~~t~e gap. Likewise, if the level of business skills is low, then training ~~~-ente~~se owners is identified as part of the inlmstructure gap. The list is presented to users through the interface depicted in Figure 3.

The interface explains to the users why certain infrastructure elements are deemed more important over others in strengthening the interactions between agricultural hinderland and rural market towns. This interaction helps formalize the users’ thinking about alternative infrastructure investment packages for the selected RTPC.

RTPC Decision Rules The second set of rules captures the dynamics of rural service centers. This set of expert

decision rules is used in congruence with the general economic forecasting model to identify potential consequences of alternative infrastructure investments. The decision rules capture the impacts of the investment on the ability of the town to carry out the functions of a rural service center, and provide information to planners about the major constraints and opportunities which largely determine which infrastructure package is feasible.

There are approximately 170 decision rules representing the social, technological, ecologic, economic, and political aspects of rural service towns which must be considered when evaluating the impacts from infrastructure investments. Decision rules are a useful representation of these aspects and the interrelationships which exist among and between them and the infrastructure investment, which are not captured in the model base. For example, the economic forecasting model of Kutus does not capture the important relationship between the farm and non- farm sector. Therefore, a decision rule is developed to capture this dynamic relationship.

The rule reads: If micro_inerease is yes, then pot_agro~ro~ is yes; and If agro_increase is yes, then micro_pot_growth is yes.

The rule suggests that if an infrastructure investment has a positive impact on the micro-enterprise sector and creates an increase in productivity (micro-increase), then consequently the agri-

426 D. Fields and 7: J. Kim

cultural sector will also experience a positive impact. That is, that an increase in micro-enterprise productivity will create a situation for potential growth in the agriculture sector (pot_agroArowth), since the micro-enterprise sector wilI likely invest capital income in the agricultural sector. The reverse is also true. If an increase in agricultural productivity is experienced, then it will create potential for a positive impact for the micro-enterprise sector (microgot_growth),

Figure 6 represents the system of growth and development in rural service centers developed based on an underst~ding of their functions. The specific rules fired6 depend upon the package of infrastructure selected for investment and the conditions of the RTPC market town as defined by the data stored in the database and requested of the user during a simulation. The system is designed so that if the right conditions exist in the region, then production of the micro-enterprise and agricultural sector will increase, and the rural market service center area will expand to serve other market areas.

APPLICATION OF THE MODEL TO THE KUTUS REGION: INITIAL FINDINGS

The results of the rules fued during a simulation are written as text and are provided to the user through the interface depicted in Figure 4. The users view this text along with the results

FIGURE 6. RTPC System of Rural Growth and Development.

*II! palsany a3nwuws~u~ les!sAqd 30 a%yaed aql wql laylo s.Iolx3 dq pauraslsuo~ sy earn laymw aql pue s~o~aas amjp+3e pue lolaas as!.&alua-omy aye u! qwoid fey1 svaiI%ns uo~eur.1o3u~ pn$xal aqL X10ys lua2a33:p e s@aAar lajuaa a+_ras prns e 30 suoyaun3 ayl1IgIn3 01 snlnx 30 &qe aql uo waurlsa,w! amlan.wegur aql30 vaedurr aql sawIsw31 q3!qM uogeurJo3ty @nlxal ay IE ~001 t2 ‘JaAaMoH *luaurisaAui 3dLx aql ~0.13 AIJUX:, -gulls IIJauaq 111~ slolaas urm3 put? am~ae3nuEr.u aql leyl slsa%Zns uoyqnm!s aqA -x!puadde aql pue L am& u; pawasald a.z slp-tsal asaqA -a+god am uo$al snlnx aql uo Iaporu ikIyBaalO3 3~uIouo3a [v3aUa% aql Lq palelauai? slaaduq a~ouoaa aql ‘a%up?lp laleM ql!~

apv.Bdn pe0.1 layn?ur ueqm pm? a’arrurslp Ja$w qI!M apel8dn iaymw e ‘a8pPq B 30 luaurisalzuy a~nian~~s~~3u? ue &umsse ‘aIdumxa JOLJ -asaq lapow aqt u1o.13 s]tnsaJ zyauxnu pue aiqdw% aq$ 01 uog!ppz u: palapjsuoa aq wnur OS@ $eqi iuawsa,y amlan.woquy aql 1x1033 slaedur! ayl svasalda3 uoy2u~o3u~ Iwxal aqL *IapouI SuyswaJo3 a!urouoaa aql 111033 palvlauai?

aurowl SljH IlllEd llews

I I 1 I

, z =a,4 I L JeaA , I L 00-o I I


I Manufacture Income I

I q With RTPC Investment m Without RTPC Investment I

1 J

I Large Farm Hhs Income I

q With RTPC Investment n Without RTPC Investment I

FIGURE 7b. Economic Impact on Kutus from Infrastructure Investment (continued).

non-physical interventions to support trader organizations, or training may all provide greater simulation to economic activity in the region and its hinderland. The computer-aided expert decision support system also finds that while physical infrastructure investment provides potential for the micro-enterprise and agriculture sectors to grow, the lack of a business climate, entrepreneurial spirit, and available land constrains realization of that growth. For example, under “Land Impacts,” while the RTPC investment for a bridge increases access to the town, the existing land allocation and zoning restricts the availability of land for development.

Likewise, while the market upgrade provides for potential for the agricultural sector to grow, the micro-economic policies, lack of market information, low level of business skills practiced by the sector, and their technological know-how prevent growth from being realized. As described under “Economic Impacts,” the proposed system also recognizes the constraint realized on the micro-enterprise sector from moving the market facility from its existing location. The system also recognizes the potential consequences that might arise from increased expec- tations of market growth and in-migration on unemployment.

Applying CAI to Rural Development in Kenya 429

[Balanceof *

0.00

-10000.00

-20000.00

-30000.00

-40000.00

-50000.00

q With RTPC Investment m ~thout RTPC Investment

FIGURE 7~. Economic Impact on Kutus from infrastructure Investment (conttnue~.

These findings are critical to successful implementation of the RTPC program. In particular, given that there exist government policies to strengthen local authorities and to promote non- farm employment opportunities, it is important that these programs be designed to complement the RTPC program. Investment resources of these programs should be directed to the Kerugoya-Kutus Town Council, the Kirinyaga Division Headquarters in Mwea (specifically, to its agriculture extension service for Kutus), and to the micro-enterprise sector in Kutus for coordination with the RTPC Project planning. As depicted in the appendix (Identified Infrastructure Gap in Kutus), there are many areas where investment in training of the local government and support to the nonfarm sector would enhance the current business climate and en~eprene~~ spirit in the Kutus region. In addition, it is impor~t that the government adopt macro- and micro- economic policies that are conducive to business.

A SUMMARY

The integration of expert systems with analytical modeling techniques into a computer-aided framework with a database function and hypermedia offers a vehicle for delivering qualitative and descriptive, as well as quantitative information to planners and administrators responsible for planning rural development projects. It supports their planning functions in five ways,

1. It provides a ranking of potential candidate rural towns according to their characteristics that describe their ability to perform the functions of rural service centers.

2. It defines a list of infras~c~e (social and physical) that is missing in the selected RTPC and explains the importance of each element to growth and development of rural service centers.

3. It allows users to design alternative infrastructure investment packages and evaluate the consequences from the investment in the rural town under different assumptions.

4. It provides users with qualitative and quantitative information that describes the feasibility of alternative infrastructure investment packages.

5. It provides insights to users of the potential benefits from integrating the RTPC program with the other rural-urban strategies.

The enormity of problems currently facing devel#pin~ countries in their pursuit for progress and development requires new ideas and approaches. The information age has provided new computer technologies that with imagination can be turned into effective tools for transferring knowledge to the institutions of developing countries responsible for meeting the demands of development. Assisting institutions to fulfill their role is best achieved through practice and successful experience. Expert decision support systems provide a framework from within which planners and administrators of institutions can learn, and practice fist hand, planning skills and managerial techniques as they apply to the problems they confront. These systems can be developed and be used to complement other means of ~nstitu~on~ development used by donor agen- cies. The s~engthen~ng of public admin~tra~~e infras~~ture can lead to greater effectiveness in resource use and to higher rewards in development efforts. A new energy is needed to support additions development research and fielding of expert decision support systems.

NOTES

1. See, for instance, pp. 13a and 34a of Evans (1986, October). 2. Unique characteristics and conditions were identified &am a survey of existing rnral service towns and market

towns (the next level of the urban hierarchy) in Kenya by this author. The basis of this survey was fram the initial index used by the MPND to select RTPCs in each district and numerous reports evaluating its performance. In addition to this field work, a number of studies were con&ted, including that by Matrix Development Consultants (1990). prepared for the U.S. Agency for International Development QJSAID)/Regional Housing and Urban Development Organization, and Abt Associates Inc. (1988). prepared for the USAID/Office of Housing and Urban Progmms.

3. See, for instance, Evans (1986, October), Smoke (1989, July; 1989, Sep~~r~, ~ny~go (1989, Novembers, and Smoke and Wheeler (1990, March).

4: r_Jnder a research and field support project of the ~S~~~au for Science and Technology and Office of Rural _____.__. and I~ti~tio~ ~v~lopment.

5. These coefficients are assumed using research by USAID of the impacts from ~nfr~~~ture on Karatina. Karatina is a market town in Kenya in close proximity to the Kirinyaga District that has matured into a rural service center and is now developing to serve the functions of an intern&i&e market center.

6. This term is used to represent a situation where because there is a relationships between defined decision ruIes, if one occurs, then the other will also occur. A chain reaction may result from one rule being set to true.

REFERENCES

Abt Associates. (1988). urbanization in African development (Project 940-100). Prepared for the Office of Housing and Urban Programs, U.S. Agency for International Development.

Dervis, K., De Melo. J., & Robinson, S. (1982). General e~ailibr~a~ ideas for de~e~o~~e~t policy. New York: Cambridge University Press.

Evans, H. (1986, October). Rural trade anb ~roda~~~o~ centers: Towards a s8raZegy for j~p~e~~tat~a~ (discussion paper no. 6). Nairobi, Keny: Ministry of Planning and National Development.

~ov~ent of Kenya (1986). Economic ~aage~~& for renewed grow& (sessional paper no. 1). Nairobi, Kenyti ~ve~ent Printing Office.

Johnston, B. F., & Clark, W. (1982). Redesigning rural de~e~o~~n~: A strategic perspective. Baltimore, MD: Johns Hopkins University Press.

Lewis, B. D. (1989). ~~terse~~olai and spatial dirges in reg~o~l eco~o~nic deye~o~~~~r: A sohE a~~oan~~~g approacfr aFpl~ed fo polity questions in Katus Region, Kenya. unpublished doctoral dissertation, Come11 University, Ithaca, NY

Matrix Development Consultants. (1990, January). Kenya m&et town development program - Synthesis of market town studies. Prepared for the Regional Housing and Urban Development Organization, U.S. Agency for International Development.

Onyango, J. A. (1989, November). Jtnpuct of rhe district development fund (discussion paper no.2). Nairobi, Kenya’ Ministry of Planning and National Development.

Ron~nelll, D. A. (1985). Aspired ~~~~~ of regional analysis: A ~esfv~ew special sfady. Boulder, CO: ~estvie~ Press.

Smoke, P. (1989, Joly). An eva~~i~~ of Kenya’s rural rrade and promotion retire s&e&on process and criteria (discussion paper no. 7). Nairobi, Kenyaz Ministry of Planning and National Development.

Smoke, P, (1989, Sep~m~). District ~ve~p~n~~~progr~~ in Kenya: An ~sess~~~ of i~~le~n~ot~on progress (discussion paper no. 9). Nairobi, Kenya: Ministry of Planning and National ~ve~opment.

Smoke, P., & Wheeler+ J. (1990, March). A review of Kenya’s rural-urban bakmce strategy: U~eTly~ng ~sa~~~io~s, j~~~e~~~~io~ p~~ress fo date and an action plan for the future (discussion paper no. 13). Nairobi, Kenya: Missy of Planning and Nations ~velo~ent.

Apply@ CA/ to Rural Deveiopmenf in Kenya 431

Steinberg, E. R. (1991). Carn~a~er-assisted ~i~~t~n: A ~nthesis &theory, practice, and fec~olug~ HilIsdale, NJ: Lawrence Erlbaum Associates Publishers.

Waterman, D. (1986). A guide fo expen systems. Reading, MA: Addison-Wesley Publishing.

APPENDIX - RTPC 1MPACT FROM 1NFRASTRUCTURE INVESTMENT

Investment Assumptions:

1. Bridge 2. Market Facility Upgrade 3. Tarmac Market Roads 4. Drainage for Market Roads 5. Drainage for Market Facility

Total Cost = Kenya Shillings 21,398,OOO

This scenario replicates the current situation in Kutus.

Market Area Impacts

l Less than 5 banking days in the market town requires that many microenterprize businesses use the banking facilities in other market towns. It limits the centrality function of the market town.

l The RTPC investment to upgrade the market facility will improve the centrality functions of the market town.

l The lack of a rural roads in the market area limits access to the market town. l Increased centrality, population growth and available land make it possible for the market

area to expand its services as an RTPC.

Ag~cult~~ Impacts

l The lack of an organized farm association to market produce to other markets constrains potential growth in the agriculture sector.

l The RTPC investment to upgrade the market facility will encourage increased agriculture production.

l The lack of a rural roads in the market area constrains agriculture potential for growth. l The poor conditions of the rural roads leading to the market town discourages agriculture

growth and productivity. l An inadequate government agriculture extension service inhibits organizing and getting

better information and training to farmers around the market area.

Economic Activity Impacts

l The RTPC investment to pave the market roads will encourage economic activity in the market town.

l The lack of telephone boxes in the market town constrains the ability for the ~croente~~ze to market and obtain price infor~tion.

l The lack of direct telephone service in the market town constrains the ability for the microenterprize to market and obtain price information.

l Lack of an urban water system upgrade constrains the economic activity of the market town.

432 D. Fields and T J. Kim

l The lack of business skills in the women’s association inhibits potential growth for the microenterprize sector.

l The poor conditions of the trunk roads leading to the market town increases the trans- portation cost to and from the market town.

l The increasing population in the market town and the lack of growth in the microenterprize sector will result in increased unemployment.

l Increase number of market suppliers using the market town will generate potential for increased economic activity.

* A new market location could potentially disrupt existing economic activity in the market town given the dependence of the micro-enterprise on location to the market facility.

l The increasing population in the market town and the lack of growth in the micro- enterprise sector will result in increased unemployment.

Economic Infras~ucture Impacts

l The location of and the number of primary trunk feeder roads to the market town is suitable for an investment in a grain storage facility.

* Given the potential for population growth in the market town, the lack of street lights could lead to nighttime disobedience and crime.

Social Infrastructure Impacts

l The lack of knowledge on environmental and health concerns by the local government increases the health risks to the local population.

l The lack of a police station in the market town given population growth will increase the risk of social disobedience and criminal acts.

l Lack of an urban water system upgrade increases the health risks in the market town. * The existence of an association for the women affords the opportunity to provide in-

formation on health and environmental safety. l The existence of an association for the women affords the opportunity to provide

information on markets and pricing in the business market.

Business Climate Impacts

l The existence of an association for farmers generates opportunity to provide market, technology, and agriculture information to the agriculture sector and thus constrains the business climate.

* The lack of an association for the microenterprize sector constrains the availability of information and dampens the business climate.

* Macro-econo~c policies constr~n the business climate. l Micro-economic policies constrain the business climate.

Entrepreneurial Spirit Impacts

l The lack of business skills in the microenterprize sector inhibits their entrepreneurial behavior. * The lack of technical training to the microenterprize association inhibits their use of

technologies and potential for productivity improvements and inhibits their entrepreneurial behavior.

* The existence of a polytechnic institute in the market town affords the opportunity to train entrepreneurs in the market area business and technical skills and to provide market infor~tion and thus promote their behavior.

* The poor government attitude inhibits the en~eprene~~ behavior in the market area.

Applying CAI to Rural Development in Kenya 433

l The existence of a relatively large group of microenterprizes in the market town provides good examples for potential entrepreneurs.

* The existence of a relatively large small scale sector in the market town provides good examples for potential entrepreneurs.

Government Impacts

l Financial training for the local government will improve the management capacity of the government and improve chances for long-run growth in the market town.

* The lack of knowledge on environmental and health concerns by the local government inhibits chances for long-run growth in the market town.

l Because the local government is shared among other towns, its ability to manage the market town’s growth is weakened.

l The lack of development training for the local government given its poor attitude towards development weakens its capacity to manage growth in the market town.

Land Impacts

l The RTPC investment bridge will foster increased access to the market town. * The availability and allocation of land in the market town constrains land access in the

market town.

Documents

Application of a computer-aided expert decision support system to rural development in Kenya