The Evaluation of Policy

Given the importance of nonefficiency objectives, evaluation of the tradeoffs that arise between efficiency and nonefficiency objectives assumes particular interest in policy analysis. Because resources are in limited supply, the achievement of any particular objective will usually come at the expense of reduced activity in some other economic endeavor. The offsetting of market failures is an important exception to this generalization, because these policy interventions liberate resources from less efficient uses and thus increase the total value of economic activity. But in most cases, the attainment of objectives entails economic costs, and the assessment of these tradeoffs can yield insight about the desirability of furthering a particular objective.

A simple graphical description of the tradeoff between efficiency and nonefficiency objectives is provided in Figure 1.1. The curve ADCB portrays the maximum level of production possibilities for a country that produces two commodities, grain and cotton. Producing at world prices leads to a production pattern represented by point C (cotton and grain production are denoted as Q_1C and Q_1G, respectively) and to a consumption possibilities frontier, WCZ. By trading at world prices, the country can choose to consume at any point along WCZ. Total income of the country can be measured with respect to either commodity. In terms of grain, total purchasing power is 0W; in terms of cotton, total purchasing power is 0Z.

If the government is dissatisfied with the degree of food self-sufficiency that results from output combination Q_1C, and Q_1G, it could increase the relative price of grain. Production will then shift to point D. Because the country cannot influence world prices, the slope of the consumption possibilities frontier (WCZ) will not change. It will shift inward to YDB, however, because the frontier must intercept production point D. The country can trade only on the basis of commodities that it has available. Measured in terms of grain, the potential income of the country will fall to 0Y. But under the new policy, a larger share of grain is produced by domestic sources. The difference in total income (0W - 0Y) times the world price of grain equals the efficiency cost of pursuing the nonefficiency objective.

If higher income as well as greater self-sufficiency is desired, the policy-maker is forced to make tradeoffs between objectives. Some compromise must be reached between the desire to maximize consumption possibilities and the interest in increasing domestic food production. Figure 1.2 shows how the tradeoffs between objectives can be analyzed. The y-axis portrays the net addition to potential national income from the commodity system under study. This value, H, is net of all opportunity costs (for resources that can be employed elsewhere in the economy) and thus represents social profit. If the economy is operating at point C of Figure 1.1, each commodity system will show zero social profit. National income is maximized, and input costs exhaust all revenue.

An index of a nonefficiency objective under study is placed on the x-axis. The zero point can be taken as representative of the state of affairs in the absence of policy. For example, if self-sufficiency is the objective, the percentage share of domestic production in domestic consumption can serve as an index measure. Movements along the x-axis rightward from the intersection represent increases in the share of domestic production relative to domestic consumption; movements leftward indicate declines in the share of productionWith the graph, new commodity systems, new technologies, or new policies can then be evaluated in terms of their aggregate potential toncrease or decrease the self-sufficiency ratio and to increase or decrease national income. Each

commodity system is represented as a point on the graph. If the new commodity system can be located in quadrant I or III, choices for the policy-maker are easy. In quadrant I, no tradeoff exists between objectives. Systems in quadrant I are socially profitable (H > 0) and contribute positively to the nonefficiency objective (X > 0). Systems that occupy quadrant III should be discouraged by policymakers, since those systems decrease national income (H < 0) and do not encourage the nonefficiency objective (X < 0).

Quadrants II and IV are the areas of difficult policy choice, because they correspond to situations of tradeoffs between objectives. In quadrant II, the new situation encourages the attainment of nonefficiency objectives (X > 0), but only at a cost in potential national income (H < 0). Because H < 0, policy-makers must enact policies that subsidize the system; otherwise, production will not be undertaken by the private sector. In the grain-cotton example, this subsidy was effected by an increase in the price of grain. In quadrant IV, a socially efficient system (H > 0) contributes negatively to the nonefficiency objective but positively to national income.

Evaluation of the systems in quadrants II and IV requires knowledge of the policy-makers' preference locus-the set of points describing the policy-makers' willingness to trade off one objective for the other. Points on this locus represent the amount of income gain needed to compensate for a given reduction in the nonefficiency objective (or, conversely, the amount of gain in the nonefficiency objective that will compensate for a given loss in income). Policy-makers who place a premium on total national income (efficiency) will have a slightly sloped locus (such as FOG); those with relatively strong concerns for food self-sufficiency (nonefficiency) will have a steeply sloped locus (such as JOK).

Two types of policy interventions are needed. Systems represented by points to the right of the preference locus should be encouraged. Systems that are socially unprofitable but that contribute sufficiently to nonefficiency objectives need to be encouraged by policy so that private profitability becomes positive. If JOK represents the preference locus, systems located in the triangular region between OK and the positive x-axis would merit assistance. Points to the left of the locus indicate systems that create unacceptable tradeoffs between alternative objectives. Policy-makers should discourage systems that are socially profitable but that create too negative an impact on nonefficiency objectives. Systems located in the triangular region between JO and the negative x-axis warrant taxes so that private profitability will become negative.

In these circumstances, policy analysis appears a straightforward exercise. The analyst need only evaluate profitability and nonefficiency effects associated with commodity systems, and appropriate policy interventions are identified. Comparisons among alternative systems allow the policy analyst to identify least-cost ways of achieving nonefficiency objectives. Systems that allow attainment of the nonefficiency objective at lesser cost (or greater gain) in efficiency terms are always preferable.

The difficulty for policy analysis, however, lies in attempting to identify the exact location of the preference locus. In some cases, observation of policy actions might help to define the locus. For example, if governments vigorously tax nonfood crops and subsidize food crops, the preference locus might be something like JOK. But most situations are unlikely to be so well defined. The individuals who make policy, and their opinions about the appropriateness of various objectives, change frequently. Nor will societal preferences be uniform. As a result, a consensus on appropriate and inappropriate policy actions will not be stable; in many cases, a consensus will not even exist.

Identification of the appropriate tradeoffs between efficiency and nonefficiency is further complicated because governments hold many nonefficiency objectives and impose many policies simultaneously. Commodity policies (taxes, subsidies, and quantitative controls on commodities), macroprice policies (wage rate, interest rate, land rental rate, and exchange rate), and macroeconomic policies (fiscal and monetary management) will exert simultaneous impacts on a commodity system. The net impact of government policy-and hence the true importance of a particular objective-can be assessed only through aggregation of these incentive effects. Expansion of staple food production might be a stated objective for the agricultural sector, for example. But if producers are subjected to high net taxes on production, some skepticism is justified regarding the priority of policy-makers for this objective.

The Role of Quantitative Policy Analysis

Even if the appropriate tradeoffs between efficiency and nonefficiency objectives are not known, quantitative analysis of the economic impacts of policies retains immense importance. But rather than inform the government as to the appropriate actions it should be taking (or not taking), policy analysts provide fuel for the on-going debate between those who wish to change policies and those who wish to maintain them. Few, if any, policies are immutable, and disaggregated information about efficiency and nonefficiency effects of policy allows policymakers to form opinions about "good" and "bad" policies on an individual basis. Appropriate policy then emerges as a result of negotiation among those with potential to influence policy.

Quantitative policy analysis also plays a dynamic role in the policymaking process by ensuring that agricultural sector objectives, constraints, and policies remain consistent. The process of updating economic analyses allows policies to be altered in step with changes in the economy and in the priorities established for the agricultural sector. Particular objectives can become obsolete or inappropriate as economies grow and change. Low food prices become less important if consumer incomes increase; high producer prices may be unnecessary if farm incomes and production technologies change significantly. Constraints on objectives and policy implementation can alter as well. Developments in the transportation infrastructure, for example, can change the potential for agroindustrial development and for the introduction of new cropping opportunities and can improve the efficacy of producer price support schemes.

In addition to ensuring consistency, quantitative policy analysis can be a dynamic simulation tool to guide patterns of growth and technical change. The development of appropriate technologies has emerged as a growing concern in developing countries. Policy analysis can contribute to discussions on this topic by allowing specification of the changes in relative input requirements necessary for future production technologies. These new technologies reflect combinations of changes in yields through improved seeds and fertilizer, the introduction of new tools or machinery inputs, and changes in the relative use of labor and capital. Discussion with agricultural scientists and engineers can identify which, if any, of the alternatives are technically feasible.

The approach to policy evaluation advanced in this book is built around a simplified analytical framework, the policy analysis matrix (PAM). The method contains a number of theoretical assumptions and empirical simplifications, and a thorough understanding of its underpinnings is essential for useful application. In most situations, the advantages of the method outweigh its shortcomings. Results are comprehensible to policy-makers and yet are theoretically consistent. The method allows measurement of the effects of policy on producer income as well as identification of transfers among key interest groups-producers in agricultural systems, consumers of food, and policy-makers controlling allocations of the government budget. Results can be easily disaggregated to focus on particular regions, types of farms, or technologies. These items represent critical information for any evaluation of agricultural policy.

The PAM is composed of two sets of identities-one set defining profitabilities and the other defining the difference between private and social values. The selection of an empirical method to estimate PAM is therefore a matter of choice. Traditionally, empirical policy analysis has relied heavily on the estimates of supply and demand curves for various inputs and outputs. In principle, these estimates provide an accurate assessment of market behavior and response. But in practice, sufficient historical data of reliable quality are only rarely available. Even when parameters describing the response to output price changes can be estimated, input demands and the impact of various interventions on production costs are usually overlooked. Further, data are often not sufficiently disaggregated among regions or types of farms. Hence, analysts are unable to assess satisfactorily the impact of government policies on the behavior of a particular commodity system. The resulting analysis is incomplete and often incomprehensible to policy-makers.

This book provides an alternative approach. The methodology is based on the formulation of budgets for representative activities: farming, marketing, and processing-that compose an agricultural commodity system. Private valuations of costs and returns are altered with information about divergences so social costs and returns can be determined. These data are almost always available or can be easily collected, and evaluation can proceed in a timely manner. When reliable information is available for predicting responses of inputs and outputs to social prices, this information can be introduced into the calculation of social costs and returns. But more often, this latter set of adjustments will be made in only an approximate manner. Once these estimations are complete, policy-makers and analysts can decide whether more costly and time-consuming approaches are needed.