IMPROVING THE ANALYTICAL AND DATA FRAMEWORK OF THE GOVERNMENT SECTOR FOR NATIONAL GOALS ACCOUNTING*

Michael E. Levy

Review of Income and Wealth, 1980, vol. 26, issue 3, 293-304

Abstract: Provision of “market goods” follows the decision rules of traditional microeconomics; pricing and resource allocation for such goods tend towards Pareto optimality. The provision of “collective goods,” by contrast, depends on political (or quasi‐political) collective decision processes; beneficiaries often receive a share of collective goods free of charge or well below average or marginal (private or social) costs. No inherent tendency towards optimality may be presumed and separate analysis of collective goods becomes an essential part of national goals accounting. The national‐income‐accounts (NIA) distinction between personal consumption expenditures (PCE) and government purchases of goods and services corresponds roughly to a division between market goods bought by the consumer and a major category of “collective goods” (i.e. “public goods” provided by government). However, a significant proportion of PCE represents “collective goods” paid for by government, business, or nonprofit organizations and provided on behalf of the consumer, whereas a part of NIA government purchases represents services paid for by the consumer (i.e. “market goods”). This article develops operationally meaningful distinctions among “market goods,”“collective goods,” and “tied aid” (a mixed category with market‐good and collective‐good characteristics). These distinctions are determined by the nature of the decision processes–rather than by the characteristics of the beneficiary or the supplier. This classification is related to the national income accounts and major discrepancies are pinpointed. The blurring of the distinction among market goods, collective goods and tied aid is found to be most consequential in the NIA treatment of “education” and “medical care” services. NIA data for these two services are restructured for national goals accounting purposes in order to illustrate both the quantitative importance and the empirical feasibility of classifying benefits by their respective decision processes.

Date: 1980
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