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	Total Factor Productivity	Solow Residual
Dziedzina	Ekonomia	Ekonomia
Rodzina	Regression model	Regression model
Rok powstania	1957	1957
Twórca	Robert Solow; Caves, Christensen & Diewert	Robert Solow
Typ	Productivity measurement via index numbers and production functions	Residual measure of total factor productivity growth
Źródło pierwotne	Solow, R. M. (1957). Technical change and the aggregate production function. The Review of Economics and Statistics, 39(3), 312–320.	Solow, R. M. (1957). Technical change and the aggregate production function. The Review of Economics and Statistics, 39(3), 312–320.
Inne nazwy	TFP, Multifactor Productivity, MFP, Joint Factor Productivity	TFP Residual, Measure of Our Ignorance, Technical Change Residual, Multifactor Productivity Residual
Pokrewne	4	3
Podsumowanie	Total factor productivity (TFP), also called multifactor productivity, measures how much output an economic unit produces from a given bundle of all its inputs taken together — capital, labour, and often intermediate materials. It is the efficiency with which inputs are jointly transformed into output, and it captures everything that raises output without raising measured inputs: technology, organization, and the reallocation of resources. TFP is measured in two broad ways: the index-number approach, which forms the ratio of an aggregate output index to an aggregate input index using economically justified (superlative) weights, and the econometric production-function approach, which estimates the technology and recovers productivity as an unobserved term.	The Solow residual is the portion of output growth that is not explained by the growth of measured inputs — capital and labour — after each input's growth is weighted by its share of national income. Introduced by Robert Solow in 1957, it is the empirical counterpart of total factor productivity (TFP) growth and is computed by subtraction rather than measured directly. Because it captures everything that raises output without raising measured inputs, it has been famously described as a 'measure of our ignorance': it labels what we cannot otherwise account for, lumping together genuine technical change, efficiency gains, and pure measurement error.