Development Policy and Planning

An Introduction to Models and Techniques

Anis Chowdhury, Colin Kirkpatrick

• Publisher: Routledge
• Year: 2003
• ISBN:
  9781134858729
• Edition: 1
• Topic: Architecture
• Subtopic: Urban Planning & Landscaping

Reorientation from economic controls to a market-based approach led to significant changes in the economic policy of developing countries in the 1980s. Yet, with governments continuing to exercise economic management to accelerate growth beyond that achieved by market forces, techniques and models of development planning are still an integral feature of development policy management.
Development Policy and Planning provides a non-technical explanation of the main techniques and models used for economic policy formulation. Each technique is illustrated in application through practical examples.

1

Development Policy Analysis and Quantitative Planning Methods

Development Policy and Planning: An Overview

The role of the state in the development process is one of the oldest topics in the economics literature, and controversy continues over the relative merits of the market mechanism as opposed to state intervention. Indeed, the relationship between governments and markets is perhaps the central issue in economic development. The question, however, is not a simple choice between laissez-faire and state intervention, for it is self evident that in all economies the government must exercise some degree of economic management and control. The important question, therefore, is about the nature and quality, rather than the extent, of the state’s intervention in the economy.

Much of the discussion of markets and governments in the context of economic development has been conducted in terms of market failure. The early development economics literature of the 1950s and 1960s identified the inadequacies of the market mechanism as the major cause of economic backwardness in developing countries, and government intervention was regarded as the means of correcting these imperfections. A variety of types of market failure were identified as inhibiting both the efficient allocation of resources and the dynamic growth process. Indivisibilities in capacity, economies of scale, monopoly and oligopoly, and externalities, all resulted in market outcomes which deviated from the perfectly competitive, allocative-efficiency, welfare-maximizing equilibrium. In addition, inadequate information on future demand, lack of infrastructural capital and high-cost input supplies could each act as constraints on private investment decisions, and thereby reduce the economy’s growth rate.

These various forms of market failure provided a prima-facie case for government intervention in the market economies of the Third World. The dominant view among economists of that time was that the problems of market failure were particularly severe in the areas of infrastructure (roads, communications, power) and industrialization. Hence, much stress was put on government policy directed towards increasing capital accumulation in these sectors.

The form of government policy that was adopted by most developing countries was direct public sector investment. To achieve this required the government to raise the level of saving, to ensure that it had control over the use of these investible surpluses, and to allocate them to investment in areas where the physical capital constraint was most severe. But how should government decision makers tackle these tasks? What policies should they adopt?

These questions can best be addressed by establishing a framework for policy analysis appropriate to the mixed economies of the developing world. The form of policy analysis that was adopted in the earlier period was often described as ‘development planning’. Development planning can be defined as ‘the conscious effort of a central organization to influence, direct, and, in some cases, even control changes in the principal economic variables (e.g. GDP, consumption, investment, saving, etc.) of a certain country and region over the course of time in accordance with a predetermined set of objectives’ (Todaro 1971:1). The widespread adoption of the development planning approach to economic policy formulation in turn led to the production of national development plans. A development plan is a specific set of quantitative targets to be achieved in a given period of time, and since the early 1950s more than 300 different development plans have been formulated. Development planning can take many different forms, but it is possible to specify certain common features. A development planning exercise typically involves the use of a planning model, which specifies in quantitative terms the relationships between objectives, constraints and policy instrument variables. The model is then used to calculate a feasible or consistent solution, defined as a set of values for the policy instruments that satisfies the specified objectives and does not exceed the predetermined constraints.

The early enthusiasm for development planning was gradually supplanted by a growing sense of disillusionment, such that by the end of the 1970s many economists were talking openly of the ‘failure of planning’. Having reviewed the previous 30 years’ experience, Killick (1976:103), for example, concluded that ‘medium-term development planning has in most LDCs almost entirely failed to deliver the advantages expected of it’.

This disenchantment with development planning can be related to a number of influences. First, there was mounting evidence that actual performance often fell short of the plan targets. Second, the technical limitations of the planning techniques and models being used became increasingly evident through time. Third, the dynamic growth economies of East Asia were held up as confirmation of the superiority of a non-interventionist, market-based policy stance over the interventionist, development planning approach. The example of the Asian newly industrializing countries (NICs) in turn fuelled the more general resurgence of the neoclassical paradigm in development economics, with its emphasis on the role of the price mechanism in allocating resources to their most efficient uses.

Having moved from the dirigism of the 1950s and early 1960s to the neoclassicalism of the 1970s and 1980s, the current literature takes a more qualified, middle-ground position on the role of development planning. The failure to achieve plan targets can now be seen, in part, as the inevitable result of over ambiguous and unrealistic expectations as to what could be achieved in terms of economic growth and structural transformation. In addition, the limitations of the planning techniques became evident. First, there was a concentration on investment as the single determinant of growth, with other factors such as human capital and productivity growth being ignored. Second, the models were essentially closed economies, assuming exogenously determined exports and focusing entirely on internal policies. Third, a narrow range of objectives was considered, with the targets being specified in terms of aggregate income or employment growth, which excluded consideration of poverty alleviation or income redistribution, for example. Finally, the early models typically took prices as fixed, which ruled out the use of prices as a policy instrument, and thereby made quantity-controls the main policy planning variable.

As the techniques of development planning have evolved through time, many of these limitations of the early models have been overcome. Input-output techniques allowed the planner to consider intersectoral resource allocations. Programming models brought out the implications of different constraints, including skilled labour and foreign resources. The social accounting matrix approach provided a method of modelling the effects of various policy interventions on income distribution. The development of computable general equilibrium models introduced the possibilities of factor substitution and productivity increases, and allowed the planner to simulate the effects on the economy of specific policy instruments.

As a result of these developments, there is now a much wider range of techniques and procedures available for policy analysis. Models can more easily be designed to match the constraints and policy objectives of individual countries, rather than using a standard framework. Also, the shift towards simulating market outcomes means that policy analysis has shifted away from the setting of targets to the comparison of instruments and programmes.

A considered evaluation of the East Asian NICs’ experience makes it clear that the neoclassical litany about ‘getting the prices right’ seriously distorts reality. The evidence demonstrates that the state played an active role, which went well beyond the prescribed neutral policy regime of neoclassical economics, in planning and directing the economic growth of these newly industrializing economies. What distinguishes planning in South Korea and Taiwan from that in India, for example, is the way in which the planning was market-enhancing rather than market-supplanting. Investment was directed into areas which were judged to have long-term growth potential and policy was aimed at increasing private sector involvement in targeted areas of production. Areas for intervention were selected with the objective of achieving international standards of productive efficiency and competitiveness.

The arguments against planning, therefore, are by no means conclusive. Much of the criticism amounts to a case against a particular type of development planning which was attempted in the 1950s and 1960s. Viewed as a broader process for policy analysis, development planning continues to provide a useful framework for economic decision making and policy formulation which is still widely used in developing countries.

Characteristics of Development Planning Models

The process of planning involves the examination of a host of social and economic variables. These socio-economic variables are related to one another in a very intricate and complex manner and our understanding of the long chain of interaction becomes hazy without the aid of an analytical model. Models are needed, therefore, to analyse complex interactions between various elements which may appear to be unrelated. As Myrdal has put it:

Models are essential aids to clear thinking… The first virtue of models is that they can make explicit and rigorous what might otherwise remain implicit, vague and self-contradictory…since ordinary thinking too often proceeds by fairly simple rule of thumb and uni-causal explanations, and rarely ascends to a complex system of interdependent relationships, model-thinking may serve as a kind of thought-therapy, loosening the cramped intellectual muscles, demonstrating the falsity or doubtfulness of generalizations, and suggesting the possibility of an interdependence previously excluded. The most justifiable claims for the use of economic models are the modest ones that they are cures for excessive rigidity of thought and exercises in searching for interdependent relationships.

(Myrdal 1968:1962–3).

Thus analytical planning models have a ‘didactic’ or ‘thought-therapeutic’ value. Even though models are simplified pictures of reality, they contribute to our understanding of some essential features of that reality.

Analytical planning models also have ‘communication’ value. Many different organizations and individual agents interact in the formulation and execution of a country’s economic and social policies. Hence the ability of a planner to communicate with politicians, bureaucrats and others involved in the policy formulation process constitutes an important element in any type of planning and such communications can be enhanced by analytical planning models. A planning model specifies the relationships between the goals of the society and the instruments that are available to achieve them. By quantifying these relationships, the planners can simulate the effects of alternative policies on the societal objectives and check whether the overall plan or objectives are consistent and feasible in terms of capacity and resource constraints. The quantitative planning models therefore provide a framework within which the various agencies involved in the planning process can carry out a fruitful dialogue regarding the possibilities and trade-offs facing the nation. In short, planning models are useful precisely because they force the planners, policy makers and others involved in the planning process to set out the structure of the economy and to focus on the relationships that determine the outcome of policy changes.

A further value of analytical planning methods is that they require a comprehensive data base, which forces the economists and statisticians to assemble existing data into a consistent and accessible form, and to identify gaps where additional information is needed.

We should bear in mind, however, that exercises with analytical planning models do not provide ‘the’ solution. Such exercises only assist in finding them. To quote Kornai, a pioneer in model building and a practitioner of planning, ‘we cannot expect our model to give final, decisive answers; it can be considered an accomplishment if it only inspires interesting thoughts, if it furnishes additional points of view for a decision’ (Kornai 1975:19). Furthermore, even though model exercises are essential elements in the preparation of well-coordinated policies, they cannot do the job all by themselves. Again, in the words of Tinbergen, another pioneer in the practice of quantitative planning techniques, ‘Models constitute a framework or a skeleton and the flesh and blood will have to be added by a lot of common sense and knowledge of details’ (Tinbergen 1981:15). Therefore, the use of quantitative planning techniques cannot completely replace intuitive judgements based on experience of the working of the economic system.

Furthermore, there is no single model that can be regarded as the best. Both in theory and in practice, different types of models are suitable for examining different policy problems. Some of the model characteristics are discussed below.

Coverage

In terms of coverage or scope, planning models can be classified into: (i) overall or national models, (ii) sectoral or regional models, (iii) special models and (iv) project analysis.

The overall models deal with the entire economy and the nation’s development strategy is analysed within them. The sectoral and regional models deal with individual producing sectors and regions and can be used to examine the consistency and feasibility of the overall objectives. The special models are designed for selected aspects of the overall plan, e.g. foreign trade or manpower development. Plans are ultimately implemented through projects, and it is at this stage that project analysis is used to examine the choice of techniques, location and size of plants within the overall objectives of the national plan. The technique that is most widely used for this purpose is social cost-benefit analysis.

Aggregation

Planning models can also be classified in terms of the degree of aggregation: (i) aggregate models, (ii) main-sector models and (iii) multi-sector models. The aggregate models treat the entire economy (or a region, depending on its scope or coverage) as one producing sector and are concerned with the forecasts of such major national accounts aggregates as savings, investment and gross national product (GNP). The most representative example of this type is the Harrod-Domar model.

The main-sector models divide the economy (or a region, as the case may be) into a few producing sectors and examine the interrelationships between them. Early examples of main-sector models can be found in Arthur Lewis’s dual economy hypothesis which dichotomized the economy into a traditional (agricultural) and a modern (manufacturing) sector, and the Marxian schema of consumption and capital goods sectors. One consumption-capital goods version of the main-sector models, known as the Mahalanobis model, was used extensively in the early planning exercises in India.

The multi-sector models divide the economy or region into a large number of producing sectors. The core of such models is the Leontief input-output analysis. One of the advantages of the multi-sectoral models is that they provide systematic linkages between the overall and the sectoral plans. In addition, they provide a framework for consistency checks among the various sectoral plans.

Time

There are two aspects of the time dimension in planning models. The first is how far into the future a model is designed to project and the second is the way time is treated within a model. With respect to the first criterion, we can distinguish three broad types of planning models: (i) short-term, covering 1–3 years; (ii) medium-term, covering a 3–7 year horizon; and (iii) long-term, extending to 10 years or more. Most of the models that are discussed in this volume are intended for medium-term planning.

In the treatment of time, models can be either (i) static or (ii) dynamic. The static models compare one future date with the present: they indicate the future values of the model variables but do not describe the path of the economy between the starting and end periods. In contrast, the dynamic models incorporate endogenous variables from a number of time periods and provide information on the nature of movement of the economy from the present to some target year. Since the analysis of dynamic models requires a knowledge of difference or differential equations which are fairly advanced in level, these models are not discussed in this volume.

Behaviour

Planning models can be classified as either stochastic or deterministic systems, depending on the way in which behavioural relationships are treated. In the stochastic models, behavioural relationships, e.g. savings and investment, are estimated by using econometric models which allow for stochastic or random disturbances. Short-term macroeconomic models are in general stochastic. Hence results obtained from such models must be treated as probabilistic. In contrast, deterministic models, such as the open input-output system, do not specify any behavioural relations and instead treat them as exogenously or administratively determined.

Closure

Sometimes the number of equations is less than the number of variables in a model, i.e. some of the variables are not explained within the model. Thus, a modeller must choose the variables to be explained (endogenous variables) to close the model. The closure rules, i.e. which variables should become endogenous and which variables should be treated as exogenous, depend on the problems at hand. Accordingly, planning models can be classified into (i) open systems, (ii) closed or fully determined systems and (iii) partially determined systems. An example of the open system is the input-output model which treats the final demands as exogenous or given (i.e. not explained within the model). However, the final demands can be endogenized by using some form of Engel curve or demand function, linking expenditure with income. In this case, all the variables or unknowns can be calculated within the system once certain policy variables are specified, and the model then becomes ‘closed’, or fully determined. These models are commonly known as simulation or forecasting models. The simulation models provide alternative scenarios for different possible sets of policy or exogenous variables and thereby help evaluate outcomes of alternati...

Table of contents

• Cover
• Half Title
• Title Page
• Copyright
• Dedication
• Contents
• List of figures
• List of tables
• Preface
• 1. Development Policy Analysis and Quantitative Planning Methods
• 2. Aggregate Consistency Models
• 3. Disaggregated Consistency Models
• 4. Multi-Sectoral Models and the Social Accounting Matrix
• 5. Programming Approach to Planning
• 6. Computable General Equilibrium Models
• 7. Cost-Benefit Analysis
• Bibliography
• Index