Data envelopment analysis (DEA) is a linear programming technique that can be used to evaluate or benchmark the performance of different businesses, non-profit organizations, public sector agencies, and even national economies. DEA can compare these or any set of entities called decision-making units (DMUs) according to how well they minimize inputs in order to maximize output(s), or, in other words, evaluate the DMUs according to how efficient they are. This article uses DEA to compare and contrast 15 Organization for Economic Co-operation and Development (OECD) nations according to how efficiently they attained their national output levels through capital and labor inputs minimization at the peak of the last business cycle in 2007. Next, these efficiency scores as well as other measures of the income shares or returns to labor and capital for each economy are used in order to see if there is some type of support for an aggregate, economy-wide productivity theory of distribution. Although different measures of productivity have been used at aggregate levels to one degree or another to predict payments to factor inputs in order to assess marginal and average productivity theory, the research done for this article has not found any other works that have used DEA, and so this article seeks to make a new and perhaps unique empirical contribution to the literature on aggregate productivity theories of labor and capital income distribution.
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