Improved Economic Analysis
The Need: Econometric models are useful in improving planning and budgeting and are vital to improving the quality of analysis. They will have a serious problem because of the quality of data in Zambia. It will be difficult in a number of instances to have sufficient data points for confidence in some of the equations in a macro-economic model. The aphorism of "garbage in, garbage out" is an extreme statement, but the lack of sufficient quality in the macro-economic analysis acts as an obstacle. It is for this reason that improvement in data is also required (and is discussed in Annex B). Econometric models are neither necessary nor sufficient to improve economic analysis within National Planning. They are not a substitute for good economic analysis although such models improve the quality of the analysis. Much can be done to improve the quality of economic analysis without a macro-economic model: for example, cross-sectional (survey) data can ground such analysis in reality. What is it? Sectoral models enhance our knowledge of the economy and improve economic policy decision making. Cost-benefit or cost-effectiveness analysis does not make as many demands on data as macro-economic models. For example, analysis of the economic impact of HIV/AIDS or fertilizer subsides would be improved by a good macro-economic or CGE model but useful analysis can be done through sectoral models without using a macro-level model. If such analysis is done well it will enhance future CGE or macro-economic models. Existing data, possibly supplemented by 2007's expanded post-harvest survey are sufficient to assess the effect of large fertilizer subsidies or government's approach to maize prices. The existing MSU contract has the skills to expand their evaluation of the impact of current and proposed policy. Their analysis will be enhanced when PEMFA's macro-economic model is completed, assuming it has some behavioral equations for the agricultural private sector. In addition, as the basic PEMFA-sponsored macro-economic forecasting model becomes operational, work could be done to add additional modules that improve its ability to forecast specific problems and enhance its ability to understand the impact of economic policy changes or external shocks. For example, a module can be added that will expand on stand-alone analysis of the costs and benefits of anti-retroviral treatment to get at the macro-economic impact of such treatment (and whether it is cost effective). Analysis of the impact of different types of educational or health expenditures that use cross sectional data can later be added to macro-models. Bank of Zambia model: A model that forecasts inflation could be developed now with the Bank of Zambia to help them understand the nature of the problem and what policy variables are most likely to be effective in dealing with inflation. Such a model would be useful if the Bank decides to move toward inflation targeting. At present the IMF is recommending against such a focus, but doing the analysis now will improve the Bank's ability to influence policy and permit a smooth transition to inflation targeting later. Similarly analysis of the determinates of the foreign exchange rate could assist the Bank of Zambia in maintaining an accurate and reasonably stable foreign exchange rate. As in the previous section, such knowledge can be added to the macro-model later. Usually models designed for central banks have to be developed separately from publicly available macro-economic models. Central banks seldom want their models and forecasts to be made public before they are ready to disclose their analysis and policy. At the same time, there are positive policy implications in the public knowing that the Bank is analyzing inflation or foreign exchange rate's instabilit download
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