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Cobb–Douglas production function

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Macroeconomic formula of productivity
Wire-grid Cobb–Douglas production surface with isoquants
A two-input Cobb–Douglas production function with isoquants

In economics and econometrics, the Cobb–Douglas production function is a particular functional form of the production function, widely used to represent the technological relationship between the amounts of two or more inputs (particularly physical capital and labor) and the amount of output that can be produced by those inputs. The Cobb–Douglas form was developed and tested against statistical evidence by Charles Cobb and Paul Douglas between 1927 and 1947;[1] according to Douglas, the functional form itself was developed earlier by Philip Wicksteed.[2]

Formulation

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In its most standard form for production of a single good with two factors, the function is given by:

Y ( L , K ) = A L β K α {\displaystyle Y(L,K)=AL^{\beta }K^{\alpha }} {\displaystyle Y(L,K)=AL^{\beta }K^{\alpha }}

where:

  • Y = total production (the real value of all goods produced in a year or 365.25 days)
  • L = labour input (person-hours worked in a year or 365.25 days)
  • K = capital input (a measure of all machinery, equipment, and buildings; the value of capital input divided by the price of capital)[clarification needed ]
  • A = total factor productivity
  • 0 < α < 1 {\displaystyle 0<\alpha <1} {\displaystyle 0<\alpha <1} and 0 < β < 1 {\displaystyle 0<\beta <1} {\displaystyle 0<\beta <1} are the capital and labor elasticities of output, respectively. These values are constants determined by available technology.

Capital and labour are the two "factors of production" of the Cobb–Douglas production function.

History

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Paul Douglas explained that his first formulation of the Cobb–Douglas production function was developed in 1927; when seeking a functional form to relate estimates he had calculated for workers and capital, he spoke with mathematician and colleague Charles Cobb, who suggested a function of the form Y = ALβK1−β, previously used by Knut Wicksell, Philip Wicksteed, and Léon Walras, although Douglas only acknowledges Wicksteed and Walras for their contributions.[3] Not long after Knut Wicksell's death in 1926, Paul Douglas and Charles Cobb implemented the Cobb–Douglas function in their work covering the subject manner of producer theory for the first time.[4] Estimating this using least squares, he obtained a result for the exponent of labour of 0.75—which was subsequently confirmed by the National Bureau of Economic Research to be 0.741. Later work in the 1940s prompted them to allow for the exponents on K and L to vary, resulting in estimates that subsequently proved to be very close to improved measure of productivity developed at that time.[5]

A major criticism at the time was that estimates of the production function, although seemingly accurate, were based on such sparse data that it was hard to give them much credibility. Douglas remarked "I must admit I was discouraged by this criticism and thought of giving up the effort, but there was something which told me I should hold on."[5] The breakthrough came in using US census data, which was cross-sectional and provided a large number of observations. Douglas presented the results of these findings, along with those for other countries, at his 1947 address as president of the American Economic Association. Shortly afterwards, Douglas went into politics and was stricken by ill health—resulting in little further development on his side. However, two decades later, his production function was widely used, being adopted by economists such as Paul Samuelson and Robert Solow.[5] The Cobb–Douglas production function is especially notable for being the first time an aggregate or economy-wide production function had been developed, estimated, and then presented to the profession for analysis; it marked a landmark change in how economists approached macroeconomics from a microeconomics perspective.[6]

Capital and Labour Elasticity

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The factor of production elasticity of output is the percentage change in output that follows from a 1% change in that factor of production, holding constant all the other factors of production as well as the total factor productivity.

In the Cobb–Douglas production function, the capital elasticity of output is α {\displaystyle \alpha } {\displaystyle \alpha }, while the labor elasticity of output is β {\displaystyle \beta } {\displaystyle \beta }.

Y / Y K / K = α {\displaystyle {\dfrac {\partial Y/Y}{\partial {K}/K}}=\alpha } {\displaystyle {\dfrac {\partial Y/Y}{\partial {K}/K}}=\alpha }

Y / Y L / L = β {\displaystyle {\dfrac {\partial Y/Y}{\partial {L}/L}}=\beta } {\displaystyle {\dfrac {\partial Y/Y}{\partial {L}/L}}=\beta }

Proof

Y / Y K / K = Y K K Y = α A L β K α 1 K Y = α A L β K α K K Y = α Y K K Y = α {\displaystyle {\dfrac {\partial Y/Y}{\partial {K}/K}}={\dfrac {\partial Y}{\partial K}}{\dfrac {K}{Y}}=\alpha AL^{\beta }K^{\alpha -1}{\dfrac {K}{Y}}=\alpha {\dfrac {AL^{\beta }K^{\alpha }}{K}}{\dfrac {K}{Y}}=\alpha {\dfrac {Y}{K}}{\dfrac {K}{Y}}=\alpha } {\displaystyle {\dfrac {\partial Y/Y}{\partial {K}/K}}={\dfrac {\partial Y}{\partial K}}{\dfrac {K}{Y}}=\alpha AL^{\beta }K^{\alpha -1}{\dfrac {K}{Y}}=\alpha {\dfrac {AL^{\beta }K^{\alpha }}{K}}{\dfrac {K}{Y}}=\alpha {\dfrac {Y}{K}}{\dfrac {K}{Y}}=\alpha }

A similar proof holds for labour.

Example

If α = 0.45, a 1% increase in capital usage would lead to approximately a .45% increase in output.

Marginal Products

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The marginal product of capital is M P K = Y K = α Y K > 0 {\displaystyle MPK={\frac {\partial Y}{\partial K}}=\alpha {\frac {Y}{K}}>0} {\displaystyle MPK={\frac {\partial Y}{\partial K}}=\alpha {\frac {Y}{K}}>0}.

The marginal product of labor is M P L = Y L = β Y L > 0 {\displaystyle MPL={\frac {\partial Y}{\partial L}}=\beta {\frac {Y}{L}}>0} {\displaystyle MPL={\frac {\partial Y}{\partial L}}=\beta {\frac {Y}{L}}>0}.

That is, increasing capital always leads to an increase in output, increasing labor always leads to an increase in output, and increasing the total factor productivity A {\displaystyle A} {\displaystyle A} increases the marginal product of capital and the marginal product of labor.

Proof

The marginal product of capital, M P K {\displaystyle MPK} {\displaystyle MPK}, corresponds to the first derivative of the production function with respect to capital:

M P K = Y K = α A L β K α 1 = α A L β K α K = α Y K {\displaystyle MPK={\frac {\partial Y}{\partial K}}=\alpha AL^{\beta }K^{\alpha -1}=\alpha {\frac {AL^{\beta }K^{\alpha }}{K}}=\alpha {\frac {Y}{K}}} {\displaystyle MPK={\frac {\partial Y}{\partial K}}=\alpha AL^{\beta }K^{\alpha -1}=\alpha {\frac {AL^{\beta }K^{\alpha }}{K}}=\alpha {\frac {Y}{K}}}

Because α > 0 {\displaystyle \alpha >0} {\displaystyle \alpha >0} (and Y > 0 , K > 0 {\displaystyle Y>0,K>0} {\displaystyle Y>0,K>0} as well), we find out that the marginal product of capital is always positive.

Example

Suppose A = 3 , L = 25 , α = 0.5 , K = 36 , β = 0.5 {\displaystyle A=3,L=25,\alpha =0.5,K=36,\beta =0.5} {\displaystyle A=3,L=25,\alpha =0.5,K=36,\beta =0.5} (unit of measures omitted for brevity).

Production is Y = 3 25 0.5 36 0.5 = 90 $ {\displaystyle Y=3\cdot 25^{0.5}\cdot 36^{0.5}=90\$} {\displaystyle Y=3\cdot 25^{0.5}\cdot 36^{0.5}=90\$}.

Increasing capital to K = 37 {\displaystyle K=37} {\displaystyle K=37} leads to a production of 91.24 $ {\displaystyle \approx 91.24\$} {\displaystyle \approx 91.24\$}, an increase of 1.24 $ {\displaystyle 1.24\$} {\displaystyle 1.24\$}.

Law of diminishing returns

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The Cobb–Douglas production function satisfies the law of diminishing returns; that is, the marginal product of capital, while always positive, is declining. As capital increases (holding labor and total factor productivity constant), the output increases but at a diminishing rate. An analogous result holds for labor.

In formulas:

M P K K = 2 Y K 2 < 0 {\displaystyle {\frac {\partial MPK}{\partial K}}={\frac {\partial ^{2}Y}{\partial K^{2}}}<0} {\displaystyle {\frac {\partial MPK}{\partial K}}={\frac {\partial ^{2}Y}{\partial K^{2}}}<0}
M P L L = 2 Y L 2 < 0 {\displaystyle {\frac {\partial MPL}{\partial L}}={\frac {\partial ^{2}Y}{\partial L^{2}}}<0} {\displaystyle {\frac {\partial MPL}{\partial L}}={\frac {\partial ^{2}Y}{\partial L^{2}}}<0}
Proof

Taking the derivative of the marginal product of capital with respect to capital (i.e., taking the second derivative of the production function with respect to capital), we have:

M P K K = 2 Y K 2 = K ( A L β α K α 1 ) = A L β α ( α 1 ) K α 2 = α ( α 1 ) A L β K α K 2 = α ( α 1 ) Y K 2 {\displaystyle {\frac {\partial MPK}{\partial K}}={\frac {\partial ^{2}Y}{\partial K^{2}}}={\frac {\partial }{\partial K}}(AL^{\beta }\alpha K^{\alpha -1})=AL^{\beta }\alpha (\alpha -1)K^{\alpha -2}=\alpha (\alpha -1)AL^{\beta }{\frac {K^{\alpha }}{K^{2}}}=\alpha (\alpha -1){\frac {Y}{K^{2}}}} {\displaystyle {\frac {\partial MPK}{\partial K}}={\frac {\partial ^{2}Y}{\partial K^{2}}}={\frac {\partial }{\partial K}}(AL^{\beta }\alpha K^{\alpha -1})=AL^{\beta }\alpha (\alpha -1)K^{\alpha -2}=\alpha (\alpha -1)AL^{\beta }{\frac {K^{\alpha }}{K^{2}}}=\alpha (\alpha -1){\frac {Y}{K^{2}}}}

Because α < 1 {\displaystyle \alpha <1} {\displaystyle \alpha <1}, then α 1 < 0 {\displaystyle \alpha -1<0} {\displaystyle \alpha -1<0} and so M P K K < 0 {\displaystyle {\dfrac {\partial MPK}{\partial K}}<0} {\displaystyle {\dfrac {\partial MPK}{\partial K}}<0}.

Example

Suppose A = 3 , L = 25 , α = 0.5 , K = 36 , β = 0.5 {\displaystyle A=3,L=25,\alpha =0.5,K=36,\beta =0.5} {\displaystyle A=3,L=25,\alpha =0.5,K=36,\beta =0.5} (unit of measures omitted for brevity).

Production is Y = 3 25 0.5 36 0.5 = 90 $ {\displaystyle Y=3\cdot 25^{0.5}\cdot 36^{0.5}=90\$} {\displaystyle Y=3\cdot 25^{0.5}\cdot 36^{0.5}=90\$}.

Increasing capital by 10 to K = 46 {\displaystyle K=46} {\displaystyle K=46} leads to a production of 101.73 $ {\displaystyle \approx 101.73\$} {\displaystyle \approx 101.73\$}, an increase of 11.73 $ {\displaystyle 11.73\$} {\displaystyle 11.73\$}, or 11.73 $ / 90 $ 13 % {\displaystyle 11.73\$/90\$\approx 13\%} {\displaystyle 11.73\$/90\$\approx 13\%}, over the K = 36 {\displaystyle K=36} {\displaystyle K=36} case.

Further increasing capital by 10 to K = 56 {\displaystyle K=56} {\displaystyle K=56} leads to a production of 112.25 $ {\displaystyle \approx 112.25\$} {\displaystyle \approx 112.25\$}, an increase of 10.52 $ {\displaystyle 10.52\$} {\displaystyle 10.52\$}, or 10.52 $ / 101.73 $ 10 % {\displaystyle 10.52\$/101.73\$\approx 10\%} {\displaystyle 10.52\$/101.73\$\approx 10\%}, over the K = 46 {\displaystyle K=46} {\displaystyle K=46} case.

Marginal Rate of Technical Substitution

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The marginal rate of technical substitution equals:

M R T S ( K , L ) = M P K M P L = α β L K {\displaystyle MRTS(K,L)={\dfrac {MPK}{MPL}}={\dfrac {\alpha }{\beta }}{\dfrac {L}{K}}} {\displaystyle MRTS(K,L)={\dfrac {MPK}{MPL}}={\dfrac {\alpha }{\beta }}{\dfrac {L}{K}}}

Elasticity of Substitution

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The elasticity of substitution is constant and equal to 1.

Proof


σ L K = d ln ( L K ) d ln ( M R T S ) = d ln ( M R T S β α ) d ln ( M R T S ) = d ln ( M R T S β α ) d M R T S d ln ( M R T S ) d M R T S = 1 M R T S β α β α M R T S = 1 {\displaystyle \sigma _{LK}={\dfrac {d\ln({\frac {L}{K}})}{d\ln(MRTS)}}={\dfrac {d\ln(MRTS{\frac {\beta }{\alpha }})}{d\ln(MRTS)}}={\dfrac {\dfrac {d\ln(MRTS{\frac {\beta }{\alpha }})}{dMRTS}}{\dfrac {d\ln(MRTS)}{dMRTS}}}={\dfrac {1}{MRTS{\frac {\beta }{\alpha }}}}\cdot {\frac {\beta }{\alpha }}\cdot MRTS=1} {\displaystyle \sigma _{LK}={\dfrac {d\ln({\frac {L}{K}})}{d\ln(MRTS)}}={\dfrac {d\ln(MRTS{\frac {\beta }{\alpha }})}{d\ln(MRTS)}}={\dfrac {\dfrac {d\ln(MRTS{\frac {\beta }{\alpha }})}{dMRTS}}{\dfrac {d\ln(MRTS)}{dMRTS}}}={\dfrac {1}{MRTS{\frac {\beta }{\alpha }}}}\cdot {\frac {\beta }{\alpha }}\cdot MRTS=1}

Cross derivatives

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An increase in labor raises the marginal product of capital, while an increase in capital raises the marginal product of labor.

In formulas: M P K L > 0 {\displaystyle {\dfrac {\partial MPK}{\partial L}}>0} {\displaystyle {\dfrac {\partial MPK}{\partial L}}>0}; M P L K > 0 {\displaystyle {\dfrac {\partial MPL}{\partial K}}>0} {\displaystyle {\dfrac {\partial MPL}{\partial K}}>0}.

Proof


We can study what happens to the marginal product of capital when labor increases by taking the partial derivative of the marginal product of capital with respect to labor, that is, the cross-derivative of output with respect to capital and labor:

M P K L = 2 Y K L = L ( A L β α K α 1 ) = A β L β 1 α K α 1 = A α β L β K α L K = α β Y L K {\displaystyle {\dfrac {\partial MPK}{\partial L}}={\dfrac {\partial ^{2}Y}{\partial K\partial L}}={\dfrac {\partial }{\partial L}}(AL^{\beta }\alpha K^{\alpha -1})=A\beta L^{\beta -1}\alpha K^{\alpha -1}=A\alpha \beta {\dfrac {L^{\beta }K^{\alpha }}{LK}}=\alpha \beta {\dfrac {Y}{LK}}} {\displaystyle {\dfrac {\partial MPK}{\partial L}}={\dfrac {\partial ^{2}Y}{\partial K\partial L}}={\dfrac {\partial }{\partial L}}(AL^{\beta }\alpha K^{\alpha -1})=A\beta L^{\beta -1}\alpha K^{\alpha -1}=A\alpha \beta {\dfrac {L^{\beta }K^{\alpha }}{LK}}=\alpha \beta {\dfrac {Y}{LK}}}

Since M P K L > 0 {\displaystyle {\dfrac {\partial MPK}{\partial L}}>0} {\displaystyle {\dfrac {\partial MPK}{\partial L}}>0}, an increase in labor raises the marginal product of capital.

An analogous proof holds for labor.

Example

Suppose A = 3 , L = 25 , α = 0.5 , K = 36 , β = 0.5 {\displaystyle A=3,L=25,\alpha =0.5,K=36,\beta =0.5} {\displaystyle A=3,L=25,\alpha =0.5,K=36,\beta =0.5} (unit of measures omitted for brevity).

Production is Y = 3 25 0.5 36 0.5 = 90 $ {\displaystyle Y=3\cdot 25^{0.5}\cdot 36^{0.5}=90\$} {\displaystyle Y=3\cdot 25^{0.5}\cdot 36^{0.5}=90\$}.

Increasing capital by 10 to K = 46 {\displaystyle K=46} {\displaystyle K=46} leads to a production of 101.73 $ {\displaystyle \approx 101.73\$} {\displaystyle \approx 101.73\$}, an increase of 11.73 $ {\displaystyle 11.73\$} {\displaystyle 11.73\$}.

Now suppose A = 3 , L = 36 , α = 0.5 , K = 36 , β = 0.5 {\displaystyle A=3,L=36,\alpha =0.5,K=36,\beta =0.5} {\displaystyle A=3,L=36,\alpha =0.5,K=36,\beta =0.5} (unit of measures omitted for brevity).

Production is 108 $ {\displaystyle 108\$} {\displaystyle 108\$}.

Increasing capital by 10 to K = 46 {\displaystyle K=46} {\displaystyle K=46} leads to a production of 122.08 $ {\displaystyle \approx 122.08\$} {\displaystyle \approx 122.08\$}, an increase of 14.08 $ {\displaystyle 14.08\$} {\displaystyle 14.08\$}

Returns to scale

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If α + β = 1 {\displaystyle \alpha +\beta =1} {\displaystyle \alpha +\beta =1}, then returns to scale are constant, meaning that an increase of capital K and labor L by a factor k will produce an increase in output Y of a factor k, that is Y ( k L , k K ) = k Y ( L , K ) {\displaystyle Y(kL,kK)=kY(L,K)} {\displaystyle Y(kL,kK)=kY(L,K)}.[7]

If α + β < 1 {\displaystyle \alpha +\beta <1} {\displaystyle \alpha +\beta <1}, then returns to scale are decreasing, meaning that an increase of capital K and labor L by a factor k will produce an increase in output Y smaller than a factor k, that is Y ( k L , k K ) < k Y ( L , K ) {\displaystyle Y(kL,kK)<kY(L,K)} {\displaystyle Y(kL,kK)<kY(L,K)}.[7]

If α + β > 1 {\displaystyle \alpha +\beta >1} {\displaystyle \alpha +\beta >1}, then returns to scale are increasing, meaning that an increase in capital K and labor L by a factor k produce an increase in output Y greater than a factor k, that is, Y ( k L , k K ) > k Y ( L , K ) {\displaystyle Y(kL,kK)>kY(L,K)} {\displaystyle Y(kL,kK)>kY(L,K)}.[7]

Proof

Y ( k L , k K ) = A ( k L ) β ( k K ) α = A k β L β k α K α = A k α + β L β K α = k α + β Y ( L , K ) {\displaystyle Y(kL,kK)=A(kL)^{\beta }(kK)^{\alpha }=Ak^{\beta }L^{\beta }k^{\alpha }K^{\alpha }=Ak^{\alpha +\beta }L^{\beta }K^{\alpha }=k^{\alpha +\beta }Y(L,K)} {\displaystyle Y(kL,kK)=A(kL)^{\beta }(kK)^{\alpha }=Ak^{\beta }L^{\beta }k^{\alpha }K^{\alpha }=Ak^{\alpha +\beta }L^{\beta }K^{\alpha }=k^{\alpha +\beta }Y(L,K)}

Plugging in α + β = 1 {\displaystyle \alpha +\beta =1} {\displaystyle \alpha +\beta =1}:

Y ( k L , k K ) = k Y ( L , K ) {\displaystyle Y(kL,kK)=kY(L,K)} {\displaystyle Y(kL,kK)=kY(L,K)}

Remuneration under perfect competition

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Under constant returns to scale, β = 1 α {\displaystyle \beta =1-\alpha } {\displaystyle \beta =1-\alpha } and Y = L M P L + K M P K {\displaystyle Y=L\cdot MPL+K\cdot MPK} {\displaystyle Y=L\cdot MPL+K\cdot MPK}.

In a perfect competition the marginal product of a production factor equals its price. Therefore, M P K = w {\displaystyle MPK=w} {\displaystyle MPK=w} and M P K = r {\displaystyle MPK=r} {\displaystyle MPK=r} where w {\displaystyle w} {\displaystyle w} is the wage rate and r {\displaystyle r} {\displaystyle r} is the price of capital, the real interest rate.

The total production can be written as follows: Y = L w + K r {\displaystyle Y=L\cdot w+K\cdot r} {\displaystyle Y=L\cdot w+K\cdot r}.

That is, the value of production is divided between renumeration for labor and renumeration for capital.

Generalized form

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In its generalized form, the Cobb–Douglas function models more than two goods. The Cobb–Douglas function may be written as[8]

f ( x ) = A i = 1 n x i λ i , x = ( x 1 , , x n ) . {\displaystyle f(x)=A\prod _{i=1}^{n}x_{i}^{\lambda _{i}},\qquad x=(x_{1},\ldots ,x_{n}).} {\displaystyle f(x)=A\prod _{i=1}^{n}x_{i}^{\lambda _{i}},\qquad x=(x_{1},\ldots ,x_{n}).}

where

  • A is an efficiency parameter
  • n is the total number of input variables (goods)
  • x1, ..., xn are the (non-negative) quantities of good consumed, produced, etc.
  • λ i {\displaystyle \lambda _{i}} {\displaystyle \lambda _{i}} is an elasticity parameter for good i

Criticisms

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The function has been criticised for its lack of foundation. Cobb and Douglas were influenced by statistical evidence that appeared to show that labor and capital shares of total output were constant over time in developed countries; they explained this by statistical fitting least-squares regression of their production function. It is now widely accepted that labor share is declining in industrialized economies.[9] [10] The production function contains a principal assumption that may not always provide the most accurate representation of a country's productive capabilities and supply-side efficiencies. This assumption is a "constant share of labor in output," which may not be effective when applied to cases of countries whose labor markets are growing at significant rates.[11] Another issue within the fundamental composition the Cobb–Douglas production function is the presence of simultaneous equation bias. When competition is presumed, the simultaneous equation bias has impact on all function types involving firm decisions – including the Cobb–Douglas function. In some cases this simultaneous equation bias doesn't appear. However, it is apparent when least squares asymptotic approximations are used.[12]

However, many modern authors[who? ] have developed models which give microeconomically based Cobb–Douglas production functions, including many New Keynesian models.[13] It is nevertheless a mathematical mistake to assume that just because the Cobb–Douglas function applies at the microeconomic level, it also always applies at the macroeconomic level. Similarly, it is not necessarily the case that a macro Cobb–Douglas applies at the disaggregated level. An early microfoundation of the aggregate Cobb–Douglas technology based on linear activities is derived in Houthakker (1955).[14] The Cobb–Douglas production function is inconsistent with modern empirical estimates of the elasticity of substitution between capital and labor, which suggest that capital and labor are gross complements. A 2021 meta-analysis of 3186 estimates concludes that "the weight of evidence accumulated in the empirical literature emphatically rejects the Cobb–Douglas specification."[15]

In a 1974 article,[16] economist Anwar Shaikh demonstrates that any economic data, together with the assumption of a constant share of production between capital and labor and respecting the assumption of constant returns to scale, can be expressed in the form of a Cobb-Douglas production function; he shows that the Cobb-Douglas function is in fact governed by algebraic relationships concerning the distribution of value added between capital and labor, and that the production function therefore does not ultimately rely on any genuine assumption about production itself. To demonstrate this, Anwar Shaikh constructs a Cobb-Douglas function based on fictitious data (data tracing the word "Humbug"), which is strongly correlated with the underlying fictitious production function (R2 = .993). In the same article, he also shows that Robert Solow’s article ‘Technical Change and the Aggregate Production Function’[17] (1957) paved the way for the neoclassical approach in the economic analysis of growth, makes the same mistake."

Cobb–Douglas utilities

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The Cobb–Douglas function is often used as a utility function.[18] [8] Utility u ~ {\displaystyle {\tilde {u}}} {\displaystyle {\tilde {u}}} is a function of the quantities x i {\displaystyle x_{i}} {\displaystyle x_{i}} of the n {\displaystyle n} {\displaystyle n} goods consumed:

u ~ ( x ) = i = 1 n x i λ i {\displaystyle {\tilde {u}}(x)=\prod _{i=1}^{n}x_{i}^{\lambda _{i}}} {\displaystyle {\tilde {u}}(x)=\prod _{i=1}^{n}x_{i}^{\lambda _{i}}}

Utility functions represent ordinal preferences and do not have natural units, unlike production functions. As the result, a monotonic transformation of a utility function represents the same preferences. Unlike with a Cobb–Douglas production function, where the sum of the exponents determines the degree of economies of scale, the sum can be normalized to one for a utility function because normalization is a monotonic transformation of the original utility function. Thus, let us define λ = i = 1 n λ i {\displaystyle \lambda =\sum _{i=1}^{n}\lambda _{i}} {\displaystyle \lambda =\sum _{i=1}^{n}\lambda _{i}} and α i = λ i λ {\displaystyle \alpha _{i}={\frac {\lambda _{i}}{\lambda }}} {\displaystyle \alpha _{i}={\frac {\lambda _{i}}{\lambda }}}, so i = 1 n α i = 1 {\displaystyle \sum _{i=1}^{n}\alpha _{i}=1} {\displaystyle \sum _{i=1}^{n}\alpha _{i}=1}, and write the utility function as:

u ( x ) = i = 1 n x i α i {\displaystyle u(x)=\prod _{i=1}^{n}x_{i}^{\alpha _{i}}} {\displaystyle u(x)=\prod _{i=1}^{n}x_{i}^{\alpha _{i}}}

The consumer maximizes utility subject to the budget constraint that the cost of the goods is less than her wealth w {\displaystyle w} {\displaystyle w}. Letting p i {\displaystyle p_{i}} {\displaystyle p_{i}} denote the goods' prices, she solves:

max x i i = 1 n x i α i  subject to the constraint  i = 1 n p i x i = w {\displaystyle \max _{x_{i}}\prod _{i=1}^{n}x_{i}^{\alpha _{i}}\quad {\text{ subject to the constraint }}\quad \sum _{i=1}^{n}p_{i}x_{i}=w} {\displaystyle \max _{x_{i}}\prod _{i=1}^{n}x_{i}^{\alpha _{i}}\quad {\text{ subject to the constraint }}\quad \sum _{i=1}^{n}p_{i}x_{i}=w}
The Marginal Rate of Substitution between each two goods is
M R S i , j = α i α j x j x i = p i p j p j x j = p i α j α i x i {\displaystyle MRS_{i,j}={\frac {\alpha _{i}}{\alpha _{j}}}{x_{j} \over x_{i}}={p_{i} \over p_{j}}\Rightarrow p_{j}x_{j}={p_{i}\alpha _{j} \over \alpha _{i}}x_{i}} {\displaystyle MRS_{i,j}={\frac {\alpha _{i}}{\alpha _{j}}}{x_{j} \over x_{i}}={p_{i} \over p_{j}}\Rightarrow p_{j}x_{j}={p_{i}\alpha _{j} \over \alpha _{i}}x_{i}}
By inserting to the budget constrain we obtain
p i x i + j i n p i x i α j α i = w {\displaystyle p_{i}x_{i}+\textstyle \sum _{j\neq i}^{n}\displaystyle p_{i}x_{i}{\frac {\alpha _{j}}{\alpha _{i}}}=w} {\displaystyle p_{i}x_{i}+\textstyle \sum _{j\neq i}^{n}\displaystyle p_{i}x_{i}{\frac {\alpha _{j}}{\alpha _{i}}}=w}
p i x i ( 1 + j i n α j α i ) = w p i x i ( α i + j i n α j ) α i = w p i x i 1 α i = w {\displaystyle \Rightarrow p_{i}x_{i}(1+\sum _{j\neq i}^{n}{\frac {\alpha _{j}}{\alpha _{i}}})=w\Rightarrow p_{i}x_{i}{\frac {(\alpha _{i}+\sum _{j\neq i}^{n}\alpha _{j})}{\alpha _{i}}}=w\Rightarrow p_{i}x_{i}{\frac {1}{\alpha _{i}}}=w} {\displaystyle \Rightarrow p_{i}x_{i}(1+\sum _{j\neq i}^{n}{\frac {\alpha _{j}}{\alpha _{i}}})=w\Rightarrow p_{i}x_{i}{\frac {(\alpha _{i}+\sum _{j\neq i}^{n}\alpha _{j})}{\alpha _{i}}}=w\Rightarrow p_{i}x_{i}{\frac {1}{\alpha _{i}}}=w}
x i = α i w p i i {\displaystyle \Rightarrow x_{i}^{*}={\frac {\alpha _{i}w}{p_{i}}}\forall i} {\displaystyle \Rightarrow x_{i}^{*}={\frac {\alpha _{i}w}{p_{i}}}\forall i}

Note that p i x i = α i w {\displaystyle p_{i}x_{i}^{*}=\alpha _{i}w} {\displaystyle p_{i}x_{i}^{*}=\alpha _{i}w}, the consumer spends fraction α i {\displaystyle \alpha _{i}} {\displaystyle \alpha _{i}} of her wealth on good i.

Also note that each good is affected solely by its own price. That is, any two goods are not substitute goods nor complementary goods. Namely, their cross elasticity equals to zero and the cross demand function of any good is described by a vertical line.

Finally, note that when the income increase by some percent the demand for the good increase by the same percent. That is, the elasticity of the demand with respect to income equals 1 and therefore, the Engel curve is a straight line starting from the origin.

Note that this is the solution for either u ( x ) {\displaystyle u(x)} {\displaystyle u(x)} or u ~ ( x ) , {\displaystyle {\tilde {u}}(x),} {\displaystyle {\tilde {u}}(x),} since the same preferences generate the same demand.

The indirect utility function can be calculated by substituting the demands x i {\displaystyle x_{i}} {\displaystyle x_{i}} into the utility function. Define the constant K = i = 1 n α i α i {\displaystyle K=\prod _{i=1}^{n}\alpha _{i}^{\alpha _{i}}} {\displaystyle K=\prod _{i=1}^{n}\alpha _{i}^{\alpha _{i}}} and we get:

v ( p , w ) = i = 1 n ( w α i p i ) α i = i = 1 n w α i i = 1 n α i α i i = 1 n p i α i = K ( w i = 1 n p i α i ) {\displaystyle v(p,w)=\prod _{i=1}^{n}\left({\frac {w\alpha _{i}}{p_{i}}}\right)^{\alpha _{i}}={\frac {\prod _{i=1}^{n}w^{\alpha _{i}}\cdot \prod _{i=1}^{n}\alpha _{i}^{\alpha _{i}}}{\prod _{i=1}^{n}p_{i}^{\alpha _{i}}}}=K\left({\frac {w}{\prod _{i=1}^{n}p_{i}^{\alpha _{i}}}}\right)} {\displaystyle v(p,w)=\prod _{i=1}^{n}\left({\frac {w\alpha _{i}}{p_{i}}}\right)^{\alpha _{i}}={\frac {\prod _{i=1}^{n}w^{\alpha _{i}}\cdot \prod _{i=1}^{n}\alpha _{i}^{\alpha _{i}}}{\prod _{i=1}^{n}p_{i}^{\alpha _{i}}}}=K\left({\frac {w}{\prod _{i=1}^{n}p_{i}^{\alpha _{i}}}}\right)}

which is a special case of the Gorman polar form. The expenditure function is the inverse of the indirect utility function:[19] : 112 

e ( p , u ) = ( 1 / K ) i = 1 n p i α i u {\displaystyle e(p,u)=(1/K)\prod _{i=1}^{n}p_{i}^{\alpha _{i}}u} {\displaystyle e(p,u)=(1/K)\prod _{i=1}^{n}p_{i}^{\alpha _{i}}u}
The Marshallian demand function that Cobb-Douglas utility function

Various representations of the production function

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The Cobb–Douglas function form can be estimated as a linear relationship using the following expression:

ln ( Y ) = a 0 + i a i ln ( I i ) {\displaystyle \ln(Y)=a_{0}+\sum _{i}a_{i}\ln(I_{i})} {\displaystyle \ln(Y)=a_{0}+\sum _{i}a_{i}\ln(I_{i})}

where

  • Y = output {\displaystyle Y={\text{output}}} {\displaystyle Y={\text{output}}}
  • I i = inputs {\displaystyle I_{i}={\text{inputs}}} {\displaystyle I_{i}={\text{inputs}}}
  • a i = model coefficients {\displaystyle a_{i}={\text{model coefficients}}} {\displaystyle a_{i}={\text{model coefficients}}}

The model can also be written as

Y = e a 0 ( I 1 ) a 1 ( I 2 ) a 2 {\displaystyle Y=e^{a_{0}}(I_{1})^{a_{1}}\cdot (I_{2})^{a_{2}}\cdots } {\displaystyle Y=e^{a_{0}}(I_{1})^{a_{1}}\cdot (I_{2})^{a_{2}}\cdots }

As noted, the common Cobb–Douglas function used in macroeconomic modeling is

Y = K α L β {\displaystyle Y=K^{\alpha }L^{\beta }} {\displaystyle Y=K^{\alpha }L^{\beta }}

where K is capital and L is labor. When the model exponents sum to one, the production function is first-order homogeneous, which implies constant returns to scale—that is, if all inputs are scaled by a common factor greater than zero, output will be scaled by the same factor.

Relationship to the CES production function

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The constant elasticity of substitution (CES) production function (in the two-factor case) is

Y = A ( α K γ + ( 1 α ) L γ ) 1 / γ , {\displaystyle Y=A\left(\alpha K^{\gamma }+(1-\alpha )L^{\gamma }\right)^{1/\gamma },} {\displaystyle Y=A\left(\alpha K^{\gamma }+(1-\alpha )L^{\gamma }\right)^{1/\gamma },}

in which the limiting case γ = 0 corresponds to a Cobb–Douglas function, Y = A K α L 1 α , {\displaystyle Y=AK^{\alpha }L^{1-\alpha },} {\displaystyle Y=AK^{\alpha }L^{1-\alpha },} with constant returns to scale.[20]

To see this, the log of the CES function:

ln ( Y ) = ln ( A ) + 1 γ ln ( α K γ + ( 1 α ) L γ ) {\displaystyle \ln(Y)=\ln(A)+{\frac {1}{\gamma }}\ln \left(\alpha K^{\gamma }+(1-\alpha )L^{\gamma }\right)} {\displaystyle \ln(Y)=\ln(A)+{\frac {1}{\gamma }}\ln \left(\alpha K^{\gamma }+(1-\alpha )L^{\gamma }\right)}

can be taken to the limit by applying L'Hôpital's rule:

lim γ 0 ln ( Y ) = ln ( A ) + α ln ( K ) + ( 1 α ) ln ( L ) . {\displaystyle \lim _{\gamma \to 0}\ln(Y)=\ln(A)+\alpha \ln(K)+(1-\alpha )\ln(L).} {\displaystyle \lim _{\gamma \to 0}\ln(Y)=\ln(A)+\alpha \ln(K)+(1-\alpha )\ln(L).}

Therefore, Y = A K α L 1 α {\displaystyle Y=AK^{\alpha }L^{1-\alpha }} {\displaystyle Y=AK^{\alpha }L^{1-\alpha }}.

Translog production function

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The translog production function is an approximation of the CES function by a second-order Taylor polynomial in the variable γ {\displaystyle \gamma } {\displaystyle \gamma } about γ = 0 {\displaystyle \gamma =0} {\displaystyle \gamma =0}, i.e. the Cobb–Douglas case.[21] [22] The name translog stands for "transcendental logarithmic." It is often used in econometrics for the fact that it is linear in the parameters, which means ordinary least squares could be used if inputs could be assumed exogenous.

In the two-factor case above the translog production function is

ln ( Y ) = ln ( A ) + α ln ( K ) + ( 1 α ) ln ( L ) + 1 2 γ α ( 1 α ) [ ln ( K ) ln ( L ) ] 2 = ln ( A ) + a K ln ( K ) + a L ln ( L ) + b K K ln 2 ( K ) + b L L ln 2 ( L ) + b K L ln ( K ) ln ( L ) {\displaystyle {\begin{aligned}\ln(Y)&=\ln(A)+\alpha \ln(K)+(1-\alpha )\ln(L)+{\frac {1}{2}}\gamma \alpha (1-\alpha )\left[\ln(K)-\ln(L)\right]^{2}\\&=\ln(A)+a_{K}\ln(K)+a_{L}\ln(L)+b_{KK}\ln ^{2}(K)+b_{LL}\ln ^{2}(L)+b_{KL}\ln(K)\ln(L)\end{aligned}}} {\displaystyle {\begin{aligned}\ln(Y)&=\ln(A)+\alpha \ln(K)+(1-\alpha )\ln(L)+{\frac {1}{2}}\gamma \alpha (1-\alpha )\left[\ln(K)-\ln(L)\right]^{2}\\&=\ln(A)+a_{K}\ln(K)+a_{L}\ln(L)+b_{KK}\ln ^{2}(K)+b_{LL}\ln ^{2}(L)+b_{KL}\ln(K)\ln(L)\end{aligned}}}

where a K {\displaystyle a_{K}} {\displaystyle a_{K}}, a L {\displaystyle a_{L}} {\displaystyle a_{L}}, b K K {\displaystyle b_{KK}} {\displaystyle b_{KK}}, b L L {\displaystyle b_{LL}} {\displaystyle b_{LL}}, and b K L {\displaystyle b_{KL}} {\displaystyle b_{KL}} are defined appropriately. In the three factor case, the translog production function is:

ln ( Y ) = ln ( A ) + a L ln ( L ) + a K ln ( K ) + a M ln ( M ) + b L L ln 2 ( L ) + b K K ln 2 ( K ) + b M M ln 2 ( M ) + b L K ln ( L ) ln ( K ) + b L M ln ( L ) ln ( M ) + b K M ln ( K ) ln ( M ) = f ( L , K , M ) . {\displaystyle {\begin{aligned}\ln(Y)&=\ln(A)+a_{L}\ln(L)+a_{K}\ln(K)+a_{M}\ln(M)+b_{LL}\ln ^{2}(L)+b_{KK}\ln ^{2}(K)+b_{MM}\ln ^{2}(M)\\&{}\qquad \qquad +b_{LK}\ln(L)\ln(K)+b_{LM}\ln(L)\ln(M)+b_{KM}\ln(K)\ln(M)\\&=f(L,K,M).\end{aligned}}} {\displaystyle {\begin{aligned}\ln(Y)&=\ln(A)+a_{L}\ln(L)+a_{K}\ln(K)+a_{M}\ln(M)+b_{LL}\ln ^{2}(L)+b_{KK}\ln ^{2}(K)+b_{MM}\ln ^{2}(M)\\&{}\qquad \qquad +b_{LK}\ln(L)\ln(K)+b_{LM}\ln(L)\ln(M)+b_{KM}\ln(K)\ln(M)\\&=f(L,K,M).\end{aligned}}}

where A {\displaystyle A} {\displaystyle A} = total factor productivity, L {\displaystyle L} {\displaystyle L} = labor, K {\displaystyle K} {\displaystyle K} = capital, M {\displaystyle M} {\displaystyle M} = materials and supplies, and Y {\displaystyle Y} {\displaystyle Y} = output.

See also

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References

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  1. ^ Cobb, C. W.; Douglas, P. H. (1928). "A Theory of Production" (PDF). American Economic Review . 18 (Supplement): 139–165. JSTOR 1811556 . Retrieved 26 September 2016.
  2. ^ Barro, Robert J.; Sala-i-Martin, Xavier (2004). Economic Growth (Second ed.). The MIT Press. p. 29, fn. 7. ISBN 0-262-02553-1.
  3. ^ Brown, Murray (2017). "Cobb–Douglas Functions". The New Palgrave Dictionary of Economics. Palgrave Macmillan UK. pp. 1–4. doi:10.1057/978-1-349-95121-5_480-2. ISBN 978-1-349-95121-5.
  4. ^ Nechyba, Thomas J. (2017). Microeconomics : an intuitive approach with calculus (2nd ed.). Boston, MA: Cengage Learning. p. 126. ISBN 978-1-305-65046-6.
  5. ^ a b c Douglas, Paul H. (October 1976). "The Cobb-Douglas Production Function Once Again: Its History, Its Testing, and Some New Empirical Values". Journal of Political Economy. 84 (5): 903–916. doi:10.1086/260489. S2CID 154435697.
  6. ^ Filipe, Jesus; Adams, F. Gerard (2005). "The Estimation of the Cobb-Douglas Function: A Retrospective View". Eastern Economic Journal. 31 (3): 427–445. JSTOR 40326423.
  7. ^ a b c Jacques, Ian (2018). Mathematics for Economics and Business (Ninth ed.). Harlow, United Kingdom: Pearson Education. p. 168. ISBN 9781292191713.
  8. ^ a b Brown, Murray (2016年05月18日). The New Palgrave Dictionary of Economics. Springer. ISBN 9781349588022.
  9. ^ Elsby, Michael; Hobijn, Bart; Sahin, Aysegül (2013年09月01日). The Decline of the U.S. Labor Share (Report). Federal Reserve Bank of San Francisco.
  10. ^ Aum, Sangmin; Shin, Yongseok (2020). "Why Is the Labor Share Declining?". Review. 102 (4). doi:10.20955/r.102.413-28 . Retrieved 2023年08月09日.
  11. ^ Hájková, Dana; Hurník, Jaromír (October 2006). "Cobb-Douglas Production Function: The Case of a Converging Economy". Czech Journal of Economics and Finance (Finance a User). 57 (9–10): 465–476. Retrieved 25 April 2021.
  12. ^ Hoch, Irving (October 1958). "Simultaneous Equation Bias in the Context of the Cobb-Douglas Production Function". Econometrica. 26 (4): 566–578. doi:10.2307/1907517. JSTOR 1907517.
  13. ^ Walsh, Carl (2003). Monetary Theory and Policy (2nd ed.). Cambridge: MIT Press. ISBN 9780262232319.
  14. ^ Houthakker, H.S. (1955), "The Pareto Distribution and the Cobb–Douglas Production Function in Activity Analysis", The Review of Economic Studies, 23 (1): 27–31, doi:10.2307/2296148, JSTOR 2296148
  15. ^ Gechert, Havranek, Irsova, Kolcunova (2021), "Measuring capital-labor substitution: The importance of method choices and publication bias", Review of Economic Dynamics, 45: 55–82, doi:10.1016/j.red.202105003, S2CID 236400765 {{citation}}: CS1 maint: multiple names: authors list (link)
  16. ^ Shaikh, Anwar (1974), "Laws of Production and Laws of Algebra: The Humbug Production Function", The Review of Economics and Statistics, 56: 115–120, doi:10.2307/1927538
  17. ^ Solow, Robert M. (1957), "Technical Change and the Aggregate Production Function", The Review of Economics and Statistics, 39: 312–320, doi:10.2307/1926047
  18. ^ Brenes, Adrián (2011). Cobb-Douglas Utility Function. Archived from the original on 2014年10月03日. Retrieved 2011年08月11日.
  19. ^ Varian, Hal (1992). Microeconomic Analysis (Third ed.). New York: Norton. ISBN 0-393-95735-7.
  20. ^ Silberberg, Eugene; Suen, Wing (2001). "Elasticity of Substitution". The Structure of Economics: A Mathematical Analysis (Third ed.). Boston: Irwin McGraw-Hill. pp. 246–2477. ISBN 0-07-234352-4.
  21. ^ Berndt, Ernst R.; Christensen, Laurits R. (1973). "The Translog Function and the Substitution of Equipment, Structures, and Labor in U.S. manufacturing 1929–68". Journal of Econometrics . 1 (1): 81–113. doi:10.1016/0304-4076(73)90007-9.
  22. ^ Wynn, R. F.; Holden, K. (1974). An Introduction to Applied Econometric Analysis. New York: Halsted Press. pp. 62–65. ISBN 0-333-16711-2.

Further reading

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  • Renshaw, Geoff (2005). Maths for Economics. New York: Oxford University Press. pp. 516–526. ISBN 0-19-926746-4.
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