     Egwald Economics: Microeconomics

Cost Functions

by

Elmer G. Wiens

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Please show your support by joining Egwald Web Services as a Facebook Fan: Follow Elmer Wiens on Twitter: K. Translog (Transcendental Logarithmic) Cost Function

The three factor Translog production function is:

ln(q) = ln(A) + aL*ln(L) + aK*ln(K) + aM*ln(M) + bLL*ln(L)*ln(L) + bKK*ln(K)*ln(K) + bMM*ln(M)*ln(M)
+ bLK*ln(L)*ln(K) + bLM*ln(L)*ln(M) + bKM*ln(K)*ln(M)   =   f(L,K,M).
(*)

where L = labour, K = capital, M = materials and supplies, and q = product.

If we have a data set relating these inputs to output for varying levels of inputs and output, we can estimate the parameters of the Translog production function directly.

Suppose, however, we have a data set relating output quantities to total costs and input (factor) prices, wL, wK, and wM. Then, we can work with the Translog cost function to estimate the parameters of the production technology.

The three factor Translog (total) cost function is:

 ln(C(q;wL,wK,wM)) = c + cq * ln(q) + cL * ln(wL) + cK * ln(wK) + cM * log(wM)                                 + .5 * [dqq * ln(q)^2 + dLL * ln(wL)^2 + dKK * ln(wK)^2 + dMM * ln(wM)^2]                   + .5 * [(dLK + dKL) * ln(wL)*ln(wK) + (dLM + dML) * ln(wL)*ln(wM) + (dKM + dMK) * ln(wK)*log(wM)]                   + dLq * ln(wL)*ln(q) + dKq * ln(wK)*ln(q) + dMq * ln(wM)*ln(q)         (**)

an equation linear in its 18 parameters, c, cq, cL, cK, cM, dqq, dLL, dKK, dMM, dLK, dKL, dLM, dML, dKM, dMK, dLq, dKq, and dMq.

We shall use two methods to obtain estimates of the parameters:

À1: Estimate the parameters the cost function by way of its factor share functions — requires data relating output quantities to (cost minimizing) factor inputs, and input prices,

À2: Estimate the parameters of the cost function directly — requires data relating output quantities to total (minimum) cost and input prices.

We shall also:

À3:   Re-estimate the Translog Production Function — to investigate properties of the underlying technology.

À1:   Estimate the factor share equations separately.

Taking the partial derivative of the cost function, C(q;wL,wK,wM), with respect to an input price, we get the (nonlinear) factor demand function for that input:

 ∂C/∂wL = L(q; wL, wK, wM), ∂C/∂wK = K(q; wL, wK, wM), ∂C/∂wM = M(q; wL, wK, wM)

The (total) cost of producing q units of output equals the sum of the factor demand functions weighted by their input prices:

C(q;wL,wK,wM)   =   wL * L(q; wL, wK, wM) + wK * K(q; wL, wK, wM) + wM * M(q; wL, wK, wM),

Write the factor share functions as:

 sL(q;wL,wK,wM) = wL * L(q; wL, wK, wM) / C(q;wL,wK,wM) = wL * ∂C/∂wL / C(q;wL,wK,wM) = (∂ln(C)/∂wL )/ (∂ln(wL)/∂wL) = ∂ln(C)/∂ln(wL), sK(q;wL,wK,wM)= wK * K(q; wL, wK, wM) / C(q;wL,wK,wM) = wK * ∂C/∂wK / C(q;wL,wK,wM) = (∂ln(C)/∂wK )/ (∂ln(wK)/∂wK) = ∂ln(C)/∂ln(wK), sM(q;wL,wK,wM)= wM * M(q; wL, wK, wM) / C(q;wL,wK,wM) = wM * ∂C/∂wM / C(q;wL,wK,wM) = (∂ln(C)/∂wM )/ (∂ln(wL)/∂wM) = ∂ln(C)/∂ln(wM).

The Translog factor share functions are:

 sL(q;wL,wK,wM) = cL + dLq * ln(q) + dLL * ln(wL) + dLK * ln(wK) + dLM * ln(wM), sK(q;wL,wK,wM) = cK + dKq * ln(q) + dKL * ln(wL) + dKK * ln(wK) + dKM * ln(wM), sM(q;wL,wK,wM) = cM + dMq * ln(q) + dML * ln(wL) + dMK * ln(wK) + dMM * ln(wM),

three linear equations in their 15 parameters.

Since it is likely that, as measured numerically, dLK != dKL, dLM != dML, and dKM != dMK, these distinctions are maintained in the factor share equations.

Having obtained the unrestricted estimates of the coefficients of the factor share functions, we shall compute the restricted least squares estimates with dLK = dKL, dLM = dML, and dKM = dMK.

I. Duality.  The Plan:

1. Generate CES data and estimate the parameters of a Translog production function.

2. Generate cost data with the estimated Translog production function, yielding a data set relating output, q, and factor prices, wL, wK, and wM, [randomized about the base factor prices (wL*, wK*, wM*)]   to the Translog cost minimizing factor inputs, L, K, and M. The base factor prices and the randomizing distribution are specified in the form below.

3. Stage 1. Obtain the estimates of the fifteen parameters of the three factor share equations using linear multiple regression.

4. Stage 2. Substitute the values of these parameters into the Translog cost function, and estimate its remaining three parameters, c, cq, and dqq, using linear multiple regression.

The estimated coefficients of the Translog production and cost functions will vary with the parameters sigma, nu, alpha, beta and gamma of the CES production function.

CES Production Function:

q = A * [alpha * (L^-rho) + beta * (K^-rho) + gamma *(M^-rho)]^(-nu/rho) = f(L,K,M).

where L = labour, K = capital, M = materials and supplies, and q = product. The parameter nu is a measure of the economies of scale, while the parameter rho yields the elasticity of substitution:

sigma = 1/(1 + rho).

Set the parameters below to re-run with your own CES parameters.

Restrictions: .7 < nu < 1.3; .5 < sigma < 1.5;
.25 < alpha < .45, .3 < beta < .5, .2 < gamma < .35
sigma = 1 → nu = alpha + beta + gamma (Cobb-Douglas)
sigma < 1 → inputs complements; sigma > 1 → inputs substitutes
4 <= wL* <= 11,   7<= wK* <= 16,   4 <= wM* <= 10

CES Production Function Parameters
elasticity of scale parameter: nu
elasticity of substitution: sigma
alpha
beta
gamma
Base Factor Prices
 wL* wK* wM*
Distribution to Randomize Factor Prices
 Use [-2, 2] Uniform distribution     Use .25 * Normal (μ = 0, σ2 = 1)

The CES production function as specified:

q = 1 * [0.35 * (L^- 0.17647) + 0.4 * (K^- 0.17647) + 0.25 *(M^- 0.17647)]^(-1/0.17647)

II. For these coefficients of the CES production function, I generated a sequence of factor prices, outputs, and the corresponding cost minimizing inputs. Then I estimated the Translog production function using multiple regression yielding the following coefficient estimates:

QR Least Squares
Parameter Estimates
Parameter Coefficient std error t-ratio
lnA6.0E-600.581
aL0.349891039932.852
aK0.399994063895.119
aM0.250116030768.879
bLL-0.0196660-1954.4
bKK-0.0213370-5734.291
bMM-0.0164370-3243.464
bLK0.02456502137.485
bLM0.01476601173.702
bKM0.01810801742.463
 R2 = 1 R2b = 1 # obs = 182

lnA = 6.0E-6 aL = 0.349891 aK = 0.399994 aM = 0.250116
bLL = -0.019666 bKK = -0.021337 bMM = -0.016437
bLK = 0.024565 bLM = 0.014766 bKM = 0.018108

aL + aK + aM = 1
-2*bLL = 0.039331 =~ 0.039331 = bLK + bLM
-2*bKK = 0.042673 =~ 0.042673 = bLK + bKLM
-2*bMM = 0.032875 =~ 0.032875 = bLM + bKM

The estimated Translog production function:

ln(q) = 6.0E-6 + 0.349891 * ln(L) + 0.399994 * ln(K) + 0.250116 * ln(M) + -0.019666 * ln(L)*ln(L) + -0.021337 * ln(K)*ln(K) + -0.016437 * ln(M)*ln(M)
+ 0.024565 * ln(L)*ln(K) + 0.014766 * ln(L)*ln(M) + 0.018108 * ln(K)*ln(M)   =   f(L,K,M).

III. For these coefficients of the Translog production function, I generated a new sequence (displayed in the "Translog Cost Function" table) of factor prices, outputs, and the corresponding cost minimizing inputs.

 The factor prices are distributed about the base factor prices by adding a random number distributed uniformly in the [-2, 2] domain.

Then I used these data to estimate the coefficients of each factor share equation separately:

QR Least Squares
Parameter Estimates
Parameter Coefficient std error t-ratio
cL0.348484014691.53
dLq000.031
dLL0.03344406507.888
dLK-0.0209040-2412.061
dLM-0.0125470-3005.871
 R2 = 1 R2b = 1 # obs = 31

QR Least Squares
Parameter Estimates
Parameter Coefficient std error t-ratio
cK0.38994309691.732
dKq9.0E-601.965
dKL-0.0208990-2397.513
dKK0.03628902468.556
dKM-0.0153970-2174.504
 R2 = 1 R2b = 1 # obs = 31

QR Least Squares
Parameter Estimates
Parameter Coefficient std error t-ratio
cM0.26157305501.239
dMq-9.0E-60-1.679
dML-0.0125450-1217.811
dMK-0.0153840-885.575
dMM0.02794403339.575
 R2 = 1 R2b = 1 # obs = 31

The three estimated factor share functions are:

 sL(q;wL,wK,wM) = 0.348484 + 0 * ln(q) + 0.033444 * ln(wL) + -0.020904 * ln(wK) + -0.012547 * ln(wM), sK(q;wL,wK,wM) = 0.389943 + 9.0E-6 * ln(q) + -0.020899 * ln(wL) + 0.036289 * ln(wK) + -0.015397 * ln(wM), sM(q;wL,wK,wM) = 0.261573 + -9.0E-6 * ln(q) + -0.012545 * ln(wL) + -0.015384 * ln(wK) + 0.027944 * ln(wM)

As estimated, generally dLK != dKL, dLM != dML, and dKM != dMK: Young's Theorem doesn't hold without constraints across equations.

Note: C(q;wL,wK,wM) linear homogeneous in factor prices implies:

 1   =?   cL + cK + cM   =   0.348484 + 0.389943 + 0.261573   =   1 0   =?   dLL + dLK + dLM   =   0.033444 + -0.020904 + -0.012547  =   -7.0E-6 0   =?   dKL + dKK + dKM   =   -0.020899 + 0.036289 + -0.015397  =   -7.0E-6 0   =?   dML + dMK + dMM   =   -0.012545 + -0.015384 + 0.027944  =   1.4E-5 0   =?   dLq + dKq + dMq   =   0 + 9.0E-6 + -9.0E-6  =   -0

IV. The Restricted Factor Share Equations.

Having obtained the unrestricted estimates of the coefficients of the factor share functions, we compute the restricted least squares estimates with dLK = dKL, dLM = dML, and dKM = dMK.

QR Restricted Least Squares
Parameter Estimates
Parameter Coefficient std error t-ratio
cL0.3484762.4E-514441.736472
dLq04.0E-60.029711
dLL0.0334438.0E-64278.716812
dLK-0.0209017.0E-6-3002.30235
dLM-0.0125465.0E-6-2416.087803
cK0.3899413.8E-510304.003528
dKq9.0E-64.0E-62.234642
dKL-0.0209017.0E-6-3002.30235
dKK0.0362891.4E-52612.454124
dKM-0.0153946.0E-6-2623.870363
cM0.2615962.3E-511608.360755
dMq-8.0E-64.0E-6-2.059593
dML-0.0125465.0E-6-2416.087803
dMK-0.0153946.0E-6-2623.870363
dMM0.0279446.0E-64527.477567
 R2 = 1 R2b = 1 # obs = 93

dLK = dKL, dLM = dML, and dKM = dMK

The three estimated, restricted factor share functions are:

 sL(q;wL,wK,wM) = 0.348476 + 0 * ln(q) + 0.033443 * ln(wL) + -0.020901 * ln(wK) + -0.012546 * ln(wM), sK(q;wL,wK,wM) = 0.389941 + 9.0E-6 * ln(q) + -0.020901 * ln(wL) + 0.036289 * ln(wK) + -0.015394 * ln(wM), sM(q;wL,wK,wM) = 0.261596 + -8.0E-6 * ln(q) + -0.012546 * ln(wL) + -0.015394 * ln(wK) + 0.027944 * ln(wM)

Imposing the dLK = dKL, dLM = dML, and dKM = dM restrictions may distort five necessary C(q;wL,wK,wM) linear homogeneous in factor prices restraints:

 1   =?   cL + cK + cM   =   0.348476 + 0.389941 + 0.261596   =   1.000014 0   =?   dLL + dLK + dLM   =   0.033443 + -0.020901 + -0.012546  =   -4.0E-6 0   =?   dKL + dKK + dKM   =   -0.020901 + 0.036289 + -0.015394  =   -6.0E-6 0   =?   dML + dMK + dMM   =   -0.012546 + -0.015394 + 0.027944  =   4.0E-6 0   =?   dLq + dKq + dMq   =   0 + 9.0E-6 + -8.0E-6  =   1.0E-6

Let us impose these additional five restraints and re-estimate the Restricted Factor Share Equations:

QR Restricted Least Squares
Parameter Estimates
Parameter Coefficient std error t-ratio
cL0.3484671.2E-527976.697001
dLq-03.0E-6-0.092244
dLL0.0334456.0E-65150.473278
dLK-0.0208985.0E-6-4345.30699
dLM-0.0125475.0E-6-2670.703007
cK0.3899281.2E-531875.825778
dKq8.0E-63.0E-62.569375
dKL-0.0208985.0E-6-4345.30699
dKK0.0362945.0E-66619.686438
dKM-0.0153964.0E-6-3708.294328
cM0.2616051.2E-522352.766446
dMq-8.0E-63.0E-6-2.453638
dML-0.0125475.0E-6-2670.703007
dMK-0.0153964.0E-6-3708.294328
dMM0.0279435.0E-65229.343223
 R2 = 1 R2b = 1 # obs = 93
 dLK = dKL, dLM = dML, dKM = dMK 1 = cL + cK + cM 0 = dLL + dLK + dLM 0 = dKL + dKK + dKM 0 = dML + dMK + dMM 0 = dLq + dKq + dMq

The three re-estimated, restricted factor share functions are:

 sL(q;wL,wK,wM) = 0.348467 + -0 * ln(q) + 0.033445 * ln(wL) + -0.020898 * ln(wK) + -0.012547 * ln(wM), sK(q;wL,wK,wM) = 0.389928 + 8.0E-6 * ln(q) + -0.020898 * ln(wL) + 0.036294 * ln(wK) + -0.015396 * ln(wM), sM(q;wL,wK,wM) = 0.261605 + -8.0E-6 * ln(q) + -0.012547 * ln(wL) + -0.015396 * ln(wK) + 0.027943 * ln(wM)

V.  To obtain estimates of the remaining three parameters, c, cq, and dqq, write:

ln(C(q;wL,wK,wM)) - {cL * ln(wL) + cK * ln(wK) + cM * log(wM) + .5 * [dLL * ln(wL)^2 + dKK * ln(wK)^2 + dMM * ln(wM)^2]
+ .5 * [(dLK + dKL) * ln(wL)*ln(wK) + (dLM + dML) * ln(wL)*ln(wM) + (dKM + dMK) * ln(wK)*log(wM)]
+ dLq * ln(wL)*ln(q) + dKq * ln(wK)*ln(q) + dMq * ln(wM)*ln(q)}
= R(q;wL,wK,wM) = c + cq * ln(q) + .5 * dqq * ln(q)^2

Estimate the linear equation:

R(q;wL,wK,wM) = c + cq * ln(q) + .5 * dqq * ln(q)^2

to obtain c, cq, and dqq.

QR Restricted Least Squares
Parameter Estimates
Parameter Coefficient std error t-ratio
c1.08316203600758.609351
cq10111853864006.84
dqq000
 R2 = 1 R2b = 1 # obs = 31

1 = cq, 0 = dqq

VI. The Translog cost function as estimated is:

ln(C(q;wL,wK,wM)) = 1.083162 + 1 * ln(q) + 0.348467 * ln(wL) + 0.389928 * ln(wK) + 0.261605 * log(wM)
+ .5 * [0 * ln(q)^2 + 0.033445 * ln(wL)^2 + 0.036294 * ln(wK)^2 + 0.027943 * ln(wM)^2]
+ .5 * [-0.041796 * ln(wL)*ln(wK) + -0.025094 * ln(wL)*ln(wM) + -0.030792 * ln(wK)*log(wM)]
+ -0 * ln(wL)*ln(q) + 8.0E-6 * ln(wK)*ln(q) + -8.0E-6 * ln(wM)*ln(q)           (***)

VII. Check for linear homogeneity:

 1   =?   cL + cK + cM   =   0.348467 + 0.389928 + 0.261605   =   1 0   =?   dLL + dLK + dLM   =   0.033445 + -0.020898 + -0.012547  =   0 0   =?   dKL + dKK + dKM   =   -0.020898 + 0.036294 + -0.015396  =   -0 0   =?   dML + dMK + dMM   =   -0.012547 + -0.015396 + 0.027943  =   -0 0   =?   dLq + dKq + dMq   =   -0 + 8.0E-6 + -8.0E-6  =   -0

C(q;wL,wK,wM) linear homogeneous in factor prices requires for any t > 0, that C(q;wL,wK,wM) obey:

C(q;t*wL,t*wK,t*wM) = t * C(q;wL,wK,wM)

For example, with wL = 7, wK = 13, wM = 6, and t = 2:

2 * C(25; 7, 13, 6) = 1275.28 =? 1275.28 = C(25; 14, 26,12).

2 * C(30; 7, 13, 6) = 1530.34 =? 1530.34 = C(30; 14, 26,12).

VIII. Check for homotheticity:

Can we write C(q;wL,wK,wM) as the product of the unit cost function C(1;wL,wK,wM) and a suitable increasing function h(q) with h(0) = 0, and h(1) = 1:

C(q;wL,wK,wM) =? h(q) * C(1;wL,wK,wM)

To do so it is necessary that:

1   =?   cq   =   1,     0   =?   dqq  =   0,

0   =?   dLq  =   -0,     0   =?   dKq  =   8.0E-6,     0   =?   dMq  =   -8.0E-6.

For example, with wL = 7, wK = 13, wM = 6, and q = 30, try h(q) = q1/nu with nu = 1:

C(30; 7, 13, 6) = 765.17 =? 765.16 = 30 * 25.51 = 30^1/1 * C(1; 7, 13, 6).

Try it with sigma = 1!

IX. 1. The matrix ∇2C of second order partial derivatives of the cost function C(q;wL,wK,wM) is symmetric. To show this, write:

ln(C(q;wL,wK,wM) = C(q;wL,wK,wM), so C(q;wL,wK,wM) = exp(C(q;wL,wK,wM)).

 ∂2C/∂q∂wL = dLq/(q*wL) = ∂2C/∂wL∂q, ∂2C/∂q∂wK = dKq/(q*wK) = ∂2C/∂wK∂q, ∂2C/∂q∂wM = dMq/(q*wM) = ∂2C/∂wM∂q, ∂2C/∂wL∂wK = .25 * (dLK+dKL)/(wL*wK) = ∂2C/∂wK∂wL, ∂2C/∂wL∂wM = .25 * (dLM+dML)/(wL*wM) = ∂2C/∂wM∂wL, ∂2C/∂wK∂wM = .25 * (dKM+dMK)/(wL*wK) = ∂2C/∂wM∂wK.

Since the matrix ∇2C(q;wL,wK,wM) is symmetric, the matrix ∇2C(q;wL,wK,wM) is also symmetric.

2. The cost function C(q;wL,wK,wM) is a concave in factor prices if its Hessian matrix ∇2wwC of second order partial derivatives with respect to factor prices is negative semidefinite.   Following the procedure suggested by Diewert and Wales (1987), write sL = sL(q;wL,wK,wM), sK = sK(q;wL,wK,wM), and sM = sM(q;wL,wK,wM), and the matrices:

D =
 dLL dLK dLM dKL dKK dKM dML dMK dMM
S =
 sL 0 0 0 sK 0 0 0 sM
SS =
 sL*sL sL*sK sL*sM sK*sL sK*sK sK*sM sM*sL sM*sK sM*sM
W =
 wL 0 0 0 wK 0 0 0 wM

Assuming C(q;wL,wK,wM) > 0, then ∇2wwC is negative semidefinite if and only if the matrix:

H = W * ∇2wwC * W   =   D   -   S   +   SS

is negative semidefinite. See the Mathematical Notes.

The values of the second order partial derivatives depend on the amount of output, and on factor prices. As an example, consider the case where q = 30, wL = 7, wK = 13, and wM = 6:

W * ∇2wwC * W =
 -0.19014 0.11908 0.07106 0.11908 -0.20645 0.08737 0.07106 0.08737 -0.15842

The principal minors of H are H1 = -0.190137, H2 = 0.025073, and H3 = 0.

If these principal minors alternate in sign, starting with negative, with H3 <= 0, the matrix H is negative (semi)definite,
and the cost function C(q;wL,wK,wM) is a concave function in factor prices at q = 30, wL = 7, wK = 13, and wM = 6.

The eigenvalues of H are e1 = -0.3198, e2 = -0.2352, and e3 = 0.
H3 = e1 * e2 * e3 = 0.

X. The three estimated factor demand functions are obtained by:

 L(q;wL,wK,wM) = sL(q;wL,wK,wM) * C(q;wL,wK,wM) / wL, K(q;wL,wK,wM) = sK(q;wL,wK,wM) * C(q;wL,wK,wM) / wK, M(q;wL,wK,wM) = sM(q;wL,wK,wM) * C(q;wL,wK,wM) / wM.

The estimated factor demand elasticities are obtained by:

 εL,wL = ∂ln(L(q;wL,wK,wM))/∂ln(wL) = -1 + sL(q;wL,wK,wM) + dLL / sL(q;wL,wK,wM), εL,wK = ∂ln(L(q;wL,wK,wM))/∂ln(wK) = sK(q;wL,wK,wM) + dLK / sL(q;wL,wK,wM), εL,wM = ∂ln(L(q;wL,wK,wM))/∂ln(wM) = sM(q;wL,wK,wM) + dLM / sL(q;wL,wK,wM), εL,q = ∂ln(L(q;wL,wK,wM))/∂ln(q) = q * ∂ln(C)/∂q + dLq / sL(q;wL,wK,wM), etc.

For example, with wL = 7, wK = 13, wM = 6, and q = 30:

 εL,wL εL,wK εL,wM εL,q εK,wL εK,wK εK,wM εK,q εM,wL εM,wK εM,wM εM,q
 -0.5634 0.3529 0.2106 1 0.2871 -0.4977 0.2106 1 0.2868 0.3527 -0.6395 1

XI. Uzawa Partial Elasticities of Substitution:

 uLK = C(q;wL,wK,wM) * ∂L(q;wL,wK,wM)/∂wK / (L(q;wL,wK,wM) * K(q;wL,wK,wM)), uLM = C(q;wL,wK,wM) * ∂L(q;wL,wK,wM)/∂wM / (L(q;wL,wK,wM) * M(q;wL,wK,wM)), uKL = C(q;wL,wK,wM) * ∂K(q;wL,wK,wM)/∂wL / (K(q;wL,wK,wM) * L(q;wL,wK,wM)), uKM = C(q;wL,wK,wM) * ∂K(q;wL,wK,wM)/∂wM / (K(q;wL,wK,wM) * M(q;wL,wK,wM)), uML = C(q;wL,wK,wM) * ∂M(q;wL,wK,wM)/∂wL / (M(q;wL,wK,wM) * L(q;wL,wK,wM)), uMK = C(q;wL,wK,wM) * ∂M(q;wL,wK,wM)/∂wK / (M(q;wL,wK,wM) * K(q;wL,wK,wM)),

where the partial derivatives of the factor demand functions are:

 ∂L(q;wL,wK,wM)/∂wK = (dLK * C / wK + sL * K) * C / (wL * L * K), ∂L(q;wL,wK,wM)/∂wM = (dLM * C / wM + sL * M) * C / (wL * L * M), ∂K(q;wL,wK,wM)/∂wL = (dKL * C / wL + sK * L) * C / (wK * K * L), ∂K(q;wL,wK,wM)/∂wM = (dKM * C / wM + sK * M) * C / (wK * K * M), ∂M(q;wL,wK,wM)/∂wL = (dML * C / wL + sM * L) * C / (wM * M * L), ∂M(q;wL,wK,wM)/∂wK = (dMK * C / wK + sM * K) * C / (wM * M * K)

With the dLK = dKL, dLM = dML, and dKM = dMK restrictions in the factor share functions, we get (approximate?) equality of the cross partial elasticities of substitution:

uLK = uKL,    uLM = uML,  and    uKM = uMK.

as seen in the following table.

XII. Table of Results: check that the estimated Translog cost function for a given level of output agrees with the minimized cost using the Translog production function.

Translog Cost Function
À1: Estimate the cost function using restricted factor shares
CES: Returns to Scale = 1, Elasticity of Substitution = 0.85
—   Translog Production Data   —    —   Translog Cost Data   —   Factor Shares   —   Uzawa Elasticities   —   W * ∇2wwC * W
obs #qwLwKwM LK MsLKsLMsKMcostest costest Lest Kest MsLsKsMuLKuLMuKLuKMuMLuMKe1e2e3
1185.62 11.64.18 22.3113.5 21.550.850.850.85372.07372.0722.3113.521.550.3370.4210.2420.850.850.850.850.850.85 -0.322-0.2310
2196.36 13.147.36 25.8615.64 17.160.850.850.85496.22496.2225.8615.6417.160.3310.4140.2540.850.850.850.850.850.85 -0.317-0.2390
3205.38 11.547.46 28.716.81 16.320.850.850.86470.08470.0728.716.8116.320.3280.4130.2590.850.850.850.860.850.86 -0.315-0.2420
4216.66 13.16.52 27.117.08 20.730.850.850.85539.47539.4727.117.0820.730.3350.4150.2510.850.850.850.850.850.85 -0.319-0.237-0
5228.44 13.87.52 26.0819.24 21.620.850.850.85648.19648.1926.0819.2421.620.340.410.2510.850.850.850.850.850.85 -0.318-0.2380
6238.62 14.324.78 24.8518.09 30.810.850.850.85620.53620.5324.8518.0930.80.3450.4170.2370.860.850.860.840.850.84 -0.325-0.2280
7248.22 13.667.28 28.5820.79 23.810.850.850.85692.23692.2328.5820.7923.810.3390.410.250.850.850.850.850.850.85 -0.319-0.2380
8256.2 12.686.62 33.3220.32 23.680.850.850.85621.02621.0233.3220.3223.680.3330.4150.2520.850.850.850.850.850.85 -0.318-0.2380
9267.38 12.586.06 30.7421.88 27.310.850.850.85667.66667.6630.7421.8827.310.340.4120.2480.850.850.850.850.850.85 -0.32-0.236-0
10277.12 12.566.04 32.5322.49 28.110.850.850.85683.95683.9532.5322.4928.110.3390.4130.2480.850.850.850.850.850.85 -0.32-0.236-0
11285.5 13.244.8 37.9120.12 31.980.850.850.85628.41628.437.9120.1231.980.3320.4240.2440.850.850.850.850.850.85 -0.321-0.2320
12296.3 12.36.64 37.9224.06 27.240.850.850.85715.71715.7137.9224.0627.240.3340.4130.2530.850.850.850.850.850.85 -0.318-0.2380
13307 136 36.8924.41 31.590.850.850.85765.17765.1736.8924.4131.590.3370.4150.2480.850.850.850.850.850.85 -0.32-0.2350
14316.9 12.547.52 39.8226.85 27.80.850.850.85820.51820.5139.8226.8527.80.3350.410.2550.850.850.850.850.850.85 -0.317-0.240
15325.1 14.45.02 47.0621.81 35.850.850.840.85734.05734.0547.0621.8135.850.3270.4280.2450.850.840.850.850.840.85 -0.321-0.2310
16336 13.524.68 42.6323.93 39.550.850.850.85764.48764.4842.6323.9339.550.3350.4230.2420.850.850.850.850.850.85 -0.322-0.230
17345.66 11.824.74 43.3725.98 37.890.850.850.85732.15732.1543.3725.9837.890.3350.4190.2450.850.850.850.850.850.85 -0.321-0.2330
18358.2 11.645.46 37.1530.91 39.440.850.850.85879.72879.7237.1530.9139.450.3460.4090.2450.850.850.850.850.850.85 -0.321-0.2340
19365.42 14.165.08 50.9525.23 40.450.850.850.85838.88838.8850.9525.2340.450.3290.4260.2450.850.840.850.850.840.85 -0.321-0.2320
20378.7 12.847.5 42.0433.83 35.830.850.850.851068.821068.8242.0433.8335.830.3420.4060.2510.850.850.850.850.850.85 -0.318-0.2390
21387.34 13.946.78 47.8531.07 38.450.850.850.851045.021045.0247.8531.0738.450.3360.4140.2490.850.850.850.850.850.85 -0.319-0.2360
22398.88 11.887.64 42.8237.46 36.560.850.850.851104.571104.5742.8237.4536.560.3440.4030.2530.850.860.850.850.860.85 -0.317-0.240
23408.06 14.947.44 49.9333.1 40.150.850.850.851195.631195.6449.9333.140.150.3370.4140.250.850.850.850.850.850.85 -0.319-0.2370
24417.76 13.986.5 49.6433.71 43.350.850.850.851138.251138.2549.6433.7143.350.3380.4140.2480.850.850.850.850.850.85 -0.32-0.235-0
25427.8 11.085.7 45.4737.8 44.610.850.850.851027.721027.7245.4737.844.610.3450.4080.2470.850.850.850.850.850.85 -0.32-0.2360
26436.22 11.74.78 51.9133.99 48.780.850.850.85953.76953.7651.9133.9948.780.3390.4170.2440.850.850.850.850.850.85 -0.321-0.2330
27446.54 13.164.82 53.9533.35 52.520.850.850.851044.821044.8253.9533.3552.520.3380.420.2420.850.850.850.850.850.85 -0.322-0.2310
28458.1 13.565.64 51.0836.93 52.20.850.850.851208.881208.8951.0836.9352.20.3420.4140.2440.850.850.850.850.850.85 -0.322-0.233-0
29467.52 14.94.82 54.5234.14 59.740.850.850.851206.651206.6654.5234.1459.740.340.4220.2390.850.850.850.850.850.85 -0.324-0.2280
30477.16 14.067.94 62.2739.31 42.830.850.850.851338.571338.5762.2739.3142.830.3330.4130.2540.850.850.850.850.850.85 -0.317-0.239-0
31488.92 11.87.58 52.3646.24 45.180.850.860.851355.171355.1752.3646.2445.180.3450.4030.2530.850.860.850.850.860.85 -0.318-0.240
AVE:337.07 13.016.16 40.5727.1 34.940.850.850.85850.91850.9240.5727.134.940.3370.4150.2480.850.850.850.850.850.85-0.32-0.2350

À2:   Estimating the Translog cost function directly.

XIII. If we have a data set relating the input (factor) prices, wL, wK, and wM, to the total (minimum) cost of producing output for varying levels output, but we do not have data on the required levels of inputs, we can estimate the Translog cost function directly. We will use the same sequence (displayed in the "Translog Cost Function" table) of factor prices, outputs, and total (minimum) cost as used in À1. The estimated coefficients of the cost function will vary with the parameters sigma, nu, alpha, beta and gamma of the CES production function used to generate these data with the Translog production function. We impose 9 restrictions on the estimates of the parameters as specified, including the 4 necessary homothetic conditions.

SVD Restricted Least Squares
Parameter Estimates
Parameter Coefficient std error t-ratio
c1.0831562.0E-6458763.35867
cq101000
cL0.3484674.0E-688806.044984
cK0.389975.0E-671185.99064
cM0.2615636.0E-644291.053451
dqq000.425013
dLL0.0334671.0E-53289.676648
dKK0.0362827.0E-65064.026418
dMM0.0279357.0E-64234.835969
2*dLK-0.0418141.3E-5-3226.242594
2*dLM-0.025121.1E-5-2202.729212
2*dKM-0.030751.6E-5-1897.912909
dLq001000
dKq002.0E-6
dMq-00-1.0E-6
 R2 = 1 R2b = 1 # obs = 31 Observation Matrix Rank: 15
 1 = cL + cK + cM 0 = dLL + dLK + dLM 0 = dKL + dKK + dKM 0 = dML + dMK + dMM 0 = dLq + dKq + dMq 0 = dLq, 0 = dKq, 0 = dMq, 1 = cq

XIV. The Translog cost function estimated directly is:

ln(C(q;wL,wK,wM)) = 1.083156 + 1 * ln(q) + 0.348467 * ln(wL) + 0.38997 * ln(wK) + 0.261563 * log(wM)
+ .5 * [0 * ln(q)^2 + 0.033467 * ln(wL)^2 + 0.036282 * ln(wK)^2 + 0.027935 * ln(wM)^2]
+ .5 * [-0.041814 * ln(wL)*ln(wK) + -0.02512 * ln(wL)*ln(wM) + -0.03075 * ln(wK)*log(wM)]
+ 0 * ln(wL)*ln(q) + 0 * ln(wK)*ln(q) + -0 * ln(wM)*ln(q)           (***)

XV. Its three derived factor share functions are:

 sL(q;wL,wK,wM) = 0.348467 + 0 * ln(q) + 0.033467 * ln(wL) + -0.020907 * ln(wK) + -0.01256 * ln(wM), sK(q;wL,wK,wM) = 0.38997 + 0 * ln(q) + -0.020907 * ln(wL) + 0.036282 * ln(wK) + -0.015375 * ln(wM), sM(q;wL,wK,wM) = 0.261563 + -0 * ln(q) + -125.6 * ln(wL) + -0.015375 * ln(wK) + 0.027935 * ln(wM)

XVI. Notes:
1. As derived, dLK = dKL, dLM = dML, and dKM = dMK: Young's Theorem holds by construction.

2. C(q;wL,wK,wM) linear homogeneous in factor prices implies:

 1   =?   cL + cK + cM   =   0.348467 + 0.38997 + 0.261563   =   1 0   =?   dLL + dLK + dLM   =   0.033467 + -0.020907 + -0.01256  =   -0 0   =?   dKL + dKK + dKM   =   -0.020907 + 0.036282 + -0.015375  =   -0 0   =?   dML + dMK + dMM   =   -0.01256 + -0.015375 + 0.027935  =   0 0   =?   dLq + dKq + dMq   =   0 + 0 + -0  =   0

3. The matrix ∇2C of second order partial derivatives of the cost function C(q;wL,wK,wM) is symmetric.

4. The cost function C(q;wL,wK,wM) is a concave in factor prices if its Hessian matrix ∇2wwC of second order partial derivatives with respect to factor prices is negative semidefinite.   Following the procedure suggested by Diewert and Wales (1987), write sL = sL(q;wL,wK,wM), sK = sK(q;wL,wK,wM), and sM = sM(q;wL,wK,wM), and the matrices:

D =
 dLL dLK dLM dKL dKK dKM dML dMK dMM
S =
 sL 0 0 0 sK 0 0 0 sM
SS =
 sL*sL sL*sK sL*sM sK*sL sK*sK sK*sM sM*sL sM*sK sM*sM
W =
 wL 0 0 0 wK 0 0 0 wM

Assuming C(q;wL,wK,wM) > 0, then ∇2wwC is negative semidefinite if and only if the matrix:

H = W * ∇2wwC * W   =   D   -   S   +   SS

is negative semidefinite. See the Mathematical Notes.

The values of the second order partial derivatives depend on the amount of output, and on factor prices. As an example, consider the case where q = 30, wL = 7, wK = 13, and wM = 6:

W * ∇2wwC * W =
 -0.19011 0.11907 0.07104 0.11907 -0.20646 0.08739 0.07104 0.08739 -0.15843

The principal minors of H are H1 = -0.190114, H2 = 0.025073, and H3 = 0.

If these principal minors alternate in sign, starting with negative, with H3 <= 0, the matrix H is negative (semi)definite,
and the cost function C(q;wL,wK,wM) is a concave function in factor prices at q = 30, wL = 7, wK = 13, and wM = 6.

The eigenvalues of H are e1 = -0.3198, e2 = -0.2352, and e3 = 0.
H3 = e1 * e2 * e3 = 0.

XVII. Check for linear homogeneity:

C(q;wL,wK,wM) linear homogeneous in factor prices requires for any t > 0, that C(q;wL,wK,wM) obey:

C(q;t*wL,t*wK,t*wM) = t * C(q;wL,wK,wM)

For example, with wL = 7, wK = 13, wM = 6, and t = 2:

2 * C(25; 7, 13, 6) = 1275.29 =? 1275.29 = C(25; 14, 26,12).

2 * C(30; 7, 13, 6) = 1530.34 =? 1530.34 = C(30; 14, 26,12).

XVIII. Check for homotheticity:

Can we write C(q;wL,wK,wM) as the product of the unit cost function C(1;wL,wK,wM) and a suitable increasing function h(q) with h(0) = 0, and h(1) = 1:

C(q;wL,wK,wM) =? h(q) * C(1;wL,wK,wM)

To do so it is necessary that:

1   =?   cq   =   1,     0   =?   dqq  =   0,

0   =?   dLq  =   0,     0   =?   dKq  =   0,     0   =?   dMq  =   -0.

As examples:

a. with wL = 7, wK = 13, wM = 6, and q = 25, try h(q) = q1/nu with nu = 1:

C(25; 7, 13, 6) = 637.64 =? 637.64 = 25 * 25.51 = 25^1/1 * C(1; 7, 13, 6).

b. with wL = 7, wK = 13, wM = 6, and q = 30, try h(q) = q1/nu with nu = 1:

C(30; 7, 13, 6) = 765.17 =? 765.17 = 30 * 25.51 = 30^1/1 * C(1; 7, 13, 6).

Try it with sigma = 1!

XIX. The three derived factor demand functions are obtained by:

 L(q;wL,wK,wM) = sL(q;wL,wK,wM) * C(q;wL,wK,wM) / wL, K(q;wL,wK,wM) = sK(q;wL,wK,wM) * C(q;wL,wK,wM) / wK, M(q;wL,wK,wM) = sM(q;wL,wK,wM) * C(q;wL,wK,wM) / wM.

The factor demand elasticities are obtained by:

 εL,wL = ∂ln(L(q;wL,wK,wM))/∂ln(wL) = -1 + sL(q;wL,wK,wM) + dLL / sL(q;wL,wK,wM), εL,wK = ∂ln(L(q;wL,wK,wM))/∂ln(wK) = sK(q;wL,wK,wM) + dLK / sL(q;wL,wK,wM), εL,wM = ∂ln(L(q;wL,wK,wM))/∂ln(wM) = sM(q;wL,wK,wM) + dLM / sL(q;wL,wK,wM), εL,q = ∂ln(L(q;wL,wK,wM))/∂ln(q) = q * ∂ln(C)/∂q + dLq / sL(q;wL,wK,wM), etc.

For example, with wL = 7, wK = 13, wM = 6, and q = 30:

 εL,wL εL,wK εL,wM εL,q εK,wL εK,wK εK,wM εK,q εM,wL εM,wK εM,wM εM,q
 -0.5634 0.3528 0.2105 1 0.2871 -0.4977 0.2107 1 0.2868 0.3527 -0.6395 1

XX. Table of Results: check that the estimated Translog cost function for a given level of output agrees with the minimized cost using the Translog production function.

Translog Cost Function
À2: Estimate the Translog cost function directly
CES: Returns to Scale = 1, Elasticity of Substitution = 0.85
—   Translog Production Data   —    —   Translog Cost Data   —   Factor Shares   —   Uzawa Elasticities   —   W * ∇2wwC * W
obs #qwLwKwM LK MsLKsLMsKMcostest costest Lest Kest MsLsKsMuLKuLMuKLuKMuMLuMKe1e2e3
1185.62 11.64.18 22.3113.5 21.550.850.850.85372.07372.0722.3113.521.550.3370.4210.2420.850.850.850.850.850.85 -0.322-0.2310
2196.36 13.147.36 25.8615.64 17.160.850.850.85496.22496.2225.8615.6417.160.3310.4140.2540.850.850.850.850.850.85 -0.317-0.2390
3205.38 11.547.46 28.716.81 16.320.850.850.86470.08470.0828.6916.8116.320.3280.4130.2590.850.850.850.860.850.86 -0.315-0.242-0
4216.66 13.16.52 27.117.08 20.730.850.850.85539.47539.4727.117.0820.730.3350.4150.2510.850.850.850.850.850.85 -0.319-0.2370
5228.44 13.87.52 26.0819.24 21.620.850.850.85648.19648.1926.0819.2421.620.340.410.2510.850.850.850.850.850.85 -0.318-0.2380
6238.62 14.324.78 24.8518.09 30.810.850.850.85620.53620.5324.8518.0930.80.3450.4170.2370.850.850.850.840.850.84 -0.325-0.2280
7248.22 13.667.28 28.5820.79 23.810.850.850.85692.23692.2328.5820.7923.810.3390.410.250.850.850.850.850.850.85 -0.319-0.2380
8256.2 12.686.62 33.3220.32 23.680.850.850.85621.02621.0233.3220.3223.680.3330.4150.2520.850.850.850.850.850.85 -0.318-0.238-0
9267.38 12.586.06 30.7421.88 27.310.850.850.85667.66667.6630.7421.8827.310.340.4120.2480.850.850.850.850.850.85 -0.32-0.2360
10277.12 12.566.04 32.5322.49 28.110.850.850.85683.95683.9532.5322.4928.110.3390.4130.2480.850.850.850.850.850.85 -0.32-0.2360
11285.5 13.244.8 37.9120.12 31.980.850.850.85628.41628.4137.9120.1231.980.3320.4240.2440.850.850.850.850.850.85 -0.321-0.232-0
12296.3 12.36.64 37.9224.06 27.240.850.850.85715.71715.7137.9224.0627.240.3340.4130.2530.850.850.850.850.850.85 -0.318-0.2380
13307 136 36.8924.41 31.590.850.850.85765.17765.1736.8924.4131.590.3370.4150.2480.850.850.850.850.850.85 -0.32-0.2350
14316.9 12.547.52 39.8226.85 27.80.850.850.85820.51820.5139.8226.8527.80.3350.410.2550.850.850.850.850.850.85 -0.317-0.240
15325.1 14.45.02 47.0621.81 35.850.850.840.85734.05734.0547.0621.8135.850.3270.4280.2450.850.840.850.850.840.85 -0.321-0.2310
16336 13.524.68 42.6323.93 39.550.850.850.85764.48764.4842.6323.9339.550.3350.4230.2420.850.840.850.850.840.85 -0.322-0.230
17345.66 11.824.74 43.3725.98 37.890.850.850.85732.15732.1543.3725.9837.890.3350.4190.2450.850.850.850.850.850.85 -0.321-0.2330
18358.2 11.645.46 37.1530.91 39.440.850.850.85879.72879.7237.1530.9139.440.3460.4090.2450.850.850.850.850.850.85 -0.321-0.2340
19365.42 14.165.08 50.9525.23 40.450.850.850.85838.88838.8850.9525.2340.460.3290.4260.2450.850.840.850.850.840.85 -0.321-0.2320
20378.7 12.847.5 42.0433.83 35.830.850.850.851068.821068.8242.0433.8335.830.3420.4060.2510.850.850.850.850.850.85 -0.318-0.2390
21387.34 13.946.78 47.8531.07 38.450.850.850.851045.021045.0247.8531.0738.450.3360.4140.2490.850.850.850.850.850.85 -0.319-0.2360
22398.88 11.887.64 42.8237.46 36.560.850.850.851104.571104.5742.8237.4636.560.3440.4030.2530.850.860.850.850.860.85 -0.317-0.240
23408.06 14.947.44 49.9333.1 40.150.850.850.851195.631195.6349.9333.140.150.3370.4140.250.850.850.850.850.850.85 -0.319-0.2370
24417.76 13.986.5 49.6433.71 43.350.850.850.851138.251138.2549.6433.7143.350.3380.4140.2480.850.850.850.850.850.85 -0.32-0.2350
25427.8 11.085.7 45.4737.8 44.610.850.850.851027.721027.7245.4737.844.610.3450.4080.2470.850.850.850.850.850.85 -0.32-0.2360
26436.22 11.74.78 51.9133.99 48.780.850.850.85953.76953.7651.9133.9948.780.3390.4170.2440.850.850.850.850.850.85 -0.321-0.2330
27446.54 13.164.82 53.9533.35 52.520.850.850.851044.821044.8253.9533.3552.520.3380.420.2420.850.850.850.850.850.85 -0.322-0.2310
28458.1 13.565.64 51.0836.93 52.20.850.850.851208.881208.8851.0836.9352.20.3420.4140.2440.850.850.850.850.850.85 -0.322-0.2330
29467.52 14.94.82 54.5234.14 59.740.850.850.851206.651206.6554.5234.1459.740.340.4220.2390.850.850.850.850.850.85 -0.324-0.2280
30477.16 14.067.94 62.2739.31 42.830.850.850.851338.571338.5762.2739.3142.830.3330.4130.2540.850.850.850.850.850.85 -0.317-0.2390
31488.92 11.87.58 52.3646.24 45.180.850.860.851355.171355.1752.3646.2445.180.3450.4030.2530.850.860.850.850.860.85 -0.318-0.240
AVE:337.07 13.016.16 40.5727.1 34.940.850.850.85850.91850.9140.5727.134.940.3370.4150.2480.850.850.850.850.850.85-0.32-0.2350

À3:   Re-estimate the Translog Production Function.

XXI. Assuming we had a data set relating the input (factor) prices, wL, wK, and wM, to the total (minimum) cost of producing output for varying levels output, but that we did not have data on the cost minimizing levels of inputs, we estimated the Translog cost function directly. With the derived factor demand functions to determine cost minimizing inputs, we can estimate a Translog production function using the data in the Translog Cost Function table.

SVD Least Squares
Parameter Estimates
Parameter Coefficient std error t-ratio
lnA-1.1E-50-0.498
aL0.349916018271.302
aK0.399985027134.198
aM0.250107029441.251
bLL-0.0196710-2938.547
bKK-0.0213360-5315.538
bMM-0.0164370-4146.314
bLK0.02456602960.132
bLM0.01476802137.565
bKM0.01810901916.601
 R2 = 1 R2b = 1 # obs = 31 Observation Matrix Rank: 10

The estimated Translog production function using the cost data is:

ln(q) = -1.1E-5 + 0.349916 * ln(L) + 0.399985 * ln(K) + 0.250107 * ln(M) + -0.019671 * ln(L)*ln(L) + -0.021336 * ln(K)*ln(K) + -0.016437 * ln(M)*ln(M)
+ 0.024566 * ln(L)*ln(K) + 0.014768 * ln(L)*ln(M) + 0.018109 * ln(K)*ln(M)   =   f(L,K,M).

The coefficients of this production function are approximately? equal to the coefficients of the original Translog production function.

XXII. Knowing the Translog production function permits us to investigate further properties of the production / cost technology. For example, we can determine the short-run average and marginal cost functions in relation to the long-run average and marginal cost functions obtained from the Translog cost function, and the short-run Allen elasticities of substitution.

For example, if we set capital at the least cost level for q = 30, then K = 24.41, and we can solve for the least cost inputs of labour, L, and materials and supplies, M, for various levels of output, q.   (See the Translog Production Function web page.)

Translog Short Run Cost Data
CES: Returns to Scale = 1, Elasticity of Substitution = 0.85
wL = 7, wK= 13, wM = 6
qest q LK Mtotal cost ave. costmarg. cost3 factor
sLM
2 factor
sLM
1414 10.6124.41 9.09446.25 31.87 14.420.870.86
1616 13.124.41 11.22476.45 29.78 15.80.870.86
1818 15.8224.41 13.55509.43 28.3 17.180.860.86
2020 18.7624.41 16.07545.13 27.26 18.540.860.86
2222 21.9224.41 18.79583.6 26.53 19.910.860.85
2424 25.3124.41 21.71624.85 26.04 21.30.860.85
2626 28.9324.41 24.81668.72 25.72 22.70.850.85
2828 32.7824.41 28.11715.53 25.55 24.10.850.85
3030 36.8824.41 31.62765.22 25.51 25.50.850.85
3232 41.1924.41 35.32817.66 25.55 26.920.850.85
3434 45.7524.41 39.21872.92 25.67 28.360.850.85
3636 50.5524.41 43.3931.07 25.86 29.810.840.85
3838 55.624.41 47.6992.16 26.11 31.280.840.85
4040 60.8824.41 52.111056.19 26.4 32.760.840.85
4242 66.4124.41 56.821123.17 26.74 34.260.840.84
4444 72.1824.41 61.751193.17 27.12 35.770.840.84
4646 78.224.41 66.91266.22 27.53 37.290.830.84
4848 84.4724.41 72.271342.33 27.97 38.840.830.84
5050 9124.41 77.861421.55 28.43 40.40.830.84
5252 97.7824.41 83.691504 28.92 41.980.830.84

We get a U-shaped, short run average cost curve, with capital fixed.

As seen in the diagram below, the short run average cost curve is (approximately) tangent to the long run average cost curve, at q = 30.

Graph of Average Cost and Marginal Cost
Translog Cost / Production Functions - Capital Fixed Average cost function Marginal cost function L.R. Average cost function

Mathematical Notes

1. The Translog Cost Function:

 ln(C(q;wL,wK,wM)) = c + cq * ln(q) + cL * ln(wL) + cK * ln(wK) + cM * log(wM)                                 + .5 * [dqq * ln(q)^2 + dLL * ln(wL)^2 + dKK * ln(wK)^2 + dMM * ln(wM)^2]                   + .5 * [(dLK + dKL) * ln(wL)*ln(wK) + (dLM + dML) * ln(wL)*ln(wM) + (dKM + dMK) * ln(wK)*log(wM)]                   + dLq * ln(wL)*ln(q) + dKq * ln(wK)*ln(q) + dMq * ln(wM)*ln(q)         (**)

2. The Factor Share Functions:

 ∂ln(C)/∂ln(wL) = (∂ln(C)/∂wL )/ (∂ln(wL)/∂wL) = (1 / C(q;wL,wK,wM)) * ∂ln(C)/∂wL * wL = wL * L(q; wL, wK, wM) / C(q;wL,wK,wM) = sL(q;wL,wK,wM), ∂ln(C)/∂ln(wK) = (∂ln(C)/∂wK )/ (∂ln(wK)/∂wK) = (1 / C(q;wL,wK,wM)) * ∂ln(C)/∂wK * wK = wK * K(q; wL, wK, wM) / C(q;wL,wK,wM) = sK(q;wL,wK,wM), ∂ln(C)/∂ln(wM) = (∂ln(C)/∂wM )/ (∂ln(wM)/∂wM) = (1 / C(q;wL,wK,wM)) * ∂ln(C)/∂wM * wM = wM * M(q; wL, wK, wM) / C(q;wL,wK,wM) = sM(q;wL,wK,wM).

 sL(q;wL,wK,wM) = cL + dLq * ln(q) + dLL * ln(wL) + dLK * ln(wK) + dLM * ln(wM), sK(q;wL,wK,wM) = cK + dKq * ln(q) + dKL * ln(wL) + dKK * ln(wK) + dKM * ln(wM), sM(q;wL,wK,wM) = cM + dMq * ln(q) + dML * ln(wL) + dMK * ln(wK) + dMM * ln(wM),

3. The Factor Demand Functions:

 L(q;wL,wK,wM) = sL(q;wL,wK,wM) * C(q;wL,wK,wM) / wL, K(q;wL,wK,wM) = sK(q;wL,wK,wM) * C(q;wL,wK,wM) / wK, M(q;wL,wK,wM) = sM(q;wL,wK,wM) * C(q;wL,wK,wM) / wM.

4. The Factor Demand Elasticities:

 C = wL * L / sL = wK * K / sK = wM * M / sM,   → sL = (wL * L) * sK / (wK * K) = wL * L / C,     ∂L/∂wL = [(∂sL/∂wL * C + sL * ∂C/∂wL) * wL - (sL * C)(1)] / wL^2 = [(dLL/wL * wL*L/sL + sL * L) * wL - (sL * wL * L/sL)] / wL^2 = (L /wL) * (dLL/sL + sL - 1), so εL,L = ∂ln(L)/∂ln(wL) = ∂ln(L)/∂wL * ∂wL/∂ln(wL) = ∂L/∂wL * wL/L = (- 1 + sL + dLL/sL). ∂L/∂wK = [(∂sL/∂wK * C + sL * ∂C/∂wK] / wL = [dLK/wK * (wL *L / sL) + ((wL * L) * sK / (wK * K))*K] / wL = [dLK*L/(wK*sL) + L*sK/wK], so εL,K = ∂ln(L)/∂ln(wK) = ∂ln(L)/∂wK * ∂wK/∂ln(wK) = ∂L/∂wK * wK/L = (sK + dLK/sL). ∂L/∂q = [(∂sL/∂q * C + sL * ∂C/∂q] / wL = [(dLq/q)*(wL*L/sL) + (wL*L/C)*C*(∂ln(C)/∂q)]/wL, so εL,q = ∂ln(L)/∂ln(q) = ∂ln(L)/∂q * ∂q/∂ln(q) = (∂L/∂q)*(q/L) = q * ∂ln(C)/∂q + dLq / sL,

5. Cost Function C(q;wL,wK,wM) Concave in Factor Prices:

 ∂ln(C)/∂ln(wL) = sL = wL * L / C = wL * ∂C/∂wL / C → ∂C/∂wL = sL * C / wL, ∂2ln(C)/∂ln(wL)∂ln(wL) = [(∂wL/∂ln(wL) * ∂C/∂wL + wL * ∂2C/∂wL∂ln(wL)) * C - wL * ∂C/∂wL * ∂C/∂ln(wL)] / C2           = [(wL * ∂C/∂wL + wL * ∂2C/∂wL∂wL * wL) * C - wL * ∂C/∂wL * ∂C/∂wL * wL] / C2           = wL * ∂C/∂wL / C - wL * ∂C/∂wL * wL * ∂C/∂wL / C2 + wL * wL * ∂2C/∂wL∂wL / C           = sL - sL * sL + wL * wL * ∂2C/∂wL∂wL / C = dLL, so wL * wL * ∂2C/∂wL∂wL / C = dLL - sL + sL * sL ∂2ln(C)/∂ln(wL)∂ln(wK) = [(∂wL/∂ln(wK) * L + wL * ∂L/∂ln(wK)) * C - wL * L * ∂C/∂ln(wK)] / C2           = [(0 + wL * ∂2C/∂wL∂wK * wK) * C - wL * L * ∂C/∂wK * wK] / C2           = - wL * L * wK * K / C2 + wL * wK * ∂2C/∂wL∂wK / C           = - sL * sK + wL * wK * ∂2C/∂wL∂wK / C = dLK, so wL * wK * ∂2C/∂wL∂wK / C = dLK + sL * sK Copyright © Elmer G. Wiens:   Egwald Web Services All Rights Reserved.    Inquiries 