Egwald Economics - Cost Functions: Generalized CES-Translog Cost Function

Egwald Web Services
Domain Names
Web Site Design

Egwald Economics: Microeconomics

Cost Functions

by

Elmer G. Wiens

Egwald's popular web pages are provided without cost to users.
Follow Elmer Wiens on Twitter:

M. Generalized CES-Translog Cost Function

This web page replicates the procedures used to estimate the parameters of a Translog cost function to approximate a CES cost function. To determine the Translog's efficacy in estimating varying elasticities of substitution among pairs of inputs, on this web page we approximate a Generalized CES cost function with the Translog cost function.

Unlike the CES cost function, the Generalized CES cost function is not necessarily homothetic. Consequently, when we estimate the Translog cost function directly, we do not impose the homothetic conditions as we did when we approximated the CES cost function with the Translog cost function.

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 use the three factor Generalized CES production function to generate the required cost data, yielding a data set relating output, q, and factor prices, wL, wK, and wM, [randomized about the base prices (wL*, wK*, wM*)] to the Generalized CES cost minimizing factor inputs, L, K, and M. The base factor prices and the randomizing distribution are specified in the form below.

The three factor Generalized CES production function is:

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

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

The parameter nu permits one to adjust the returns to scale, while the parameter rho is the geometric mean of rhoL, rhoK, and rhoM:

rho = (rhoL * rhoK * rhoM)^^1/3.

If rho = rhoL = rhoK = rhoM, we get the standard CES production function. If also, rho = 0, ie sigma = 1/(1+rho) = 1, we get the Cobb-Douglas production function.

Generalized CES Elasticity of Scale of Production:

ε_LKM = ε(L,K,M) = (nu / rho) * (alpha * rhoL * L^^-rhoL + beta * rhoK * K^^-rhoK + gamma * rhoM * M^^-rhoM) / (alpha * L^^-rhoL + beta * K^^-rhoK + gamma * M^^-rhoM).

See the Generalized CES production function.

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

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.

II. The estimated coefficients of the Translog cost function will vary with the parameters nu, rho, rhoL, rhoK, rhoM, alpha, beta and gamma of the Generalized CES production function.

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

The restrictions ensure that the least-cost problems can be solved to obtain the underlying Generalized CES cost function, using the parameters as specified.
Intermediate (and other) values of the parameters also work.

Restrictions:
.8 < nu < 1.1;
-.6 < rhoL = rho < .6;
-.6 < rho < -.2 → rhoK = .95 * rho, rhoM = rho/.95;
-.2 <= rho < -.1 → rhoK = .9 * rho, rhoM = rho / .9;
-.1 <= rho < 0 → rhoK = .87 * rho, rhoM = rho / .87;
0 <= rho < .1 → rhoK = .7 * rho, rhoM = rho / .7;
.1 <= rho < .2 → rhoK = .67 * rho, rhoM = rho / .67;
.2 <= rho < .6 → rhoK = .6 * rho, rhoM = rho / .6;
rho = 0 → nu = alpha + beta + gamma (Cobb-Douglas)
4 <= wL* <= 11, 7<= wK* <= 16, 4 <= wM* <= 10

Generalized CES Production Function Parameters

nu:

rho:

Base Factor Prices

wL*

wK*

wM*

Distribution to Randomize Factor Prices

Use [-2, 2] Uniform distribution
Use .25 * Normal (μ = 0, σ² = 1)

The Generalized CES production function as specified:

q = 1 * [0.35 * L^^{- 0.17647} + 0.4 * K^^{- 0.11823} + 0.25 *M^^{- 0.26339}]^^(-1/0.17647) = f(L,K,M).

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

III. For these coefficients of the CES Generalized production function, I generated a sequence (displayed in the "Generalized CES-Translog Cost Function" table) of factor prices, outputs, and the corresponding cost minimizing inputs. 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
cL	0.360778	0.001	377.256
dLq	4.5E-5	0	0.51
dLL	0.034345	0	214.634
dLK	-0.013767	0	-51.859
dLM	-0.020399	0	-146.499

R2 = 0.9996

R2b = 0.9995

# obs = 31

QR Least Squares
Parameter Estimates
Parameter	Coefficient	std error	t-ratio
cK	0.272972	0.001	407.48
dKq	0.020681	0	332.099
dKL	-0.013782	0	-122.951
dKK	0.030623	0	164.674
dKM	-0.017066	0	-174.963

R2 = 0.9999

R2b = 0.9999

# obs = 31

QR Least Squares
Parameter Estimates
Parameter	Coefficient	std error	t-ratio
cM	0.36625	0.001	495.542
dMq	-0.020726	0	-301.671
dML	-0.020563	0	-166.277
dMK	-0.016856	0	-82.157
dMM	0.037466	0	348.142

R2 = 0.9999

R2b = 0.9999

# obs = 31

The three estimated factor share functions are:

sL(q;wL,wK,wM) = 0.360778 + 4.5E-5 * ln(q) + 0.034345 * ln(wL) + -0.013767 * ln(wK) + -0.020399 * ln(wM),

sK(q;wL,wK,wM) = 0.272972 + 0.020681 * ln(q) + -0.013782 * ln(wL) + 0.030623 * ln(wK) + -0.017066 * ln(wM),

sM(q;wL,wK,wM) = 0.36625 + -0.020726 * ln(q) + -0.020563 * ln(wL) + -0.016856 * ln(wK) + 0.037466 * 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 = 0.360778 + 0.272972 + 0.36625 = 1
0 =? dLL + dLK + dLM = 0.034345 + -0.013767 + -0.020399 = 0.000179
0 =? dKL + dKK + dKM = -0.013782 + 0.030623 + -0.017066 = -0.000225
0 =? dML + dMK + dMM = -0.020563 + -0.016856 + 0.037466 = 4.7E-5
0 =? dLq + dKq + dMq = 4.5E-5 + 0.020681 + -0.020726 = -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
cL	0.360969	0.000472	764.738991
dLq	4.0E-5	6.4E-5	0.627604
dLL	0.03435	0.000115	297.728869
dLK	-0.013792	9.8E-5	-140.230463
dLM	-0.020466	7.4E-5	-276.229403
cK	0.272834	0.000658	414.687343
dKq	0.020684	6.3E-5	326.171973
dKL	-0.013792	9.8E-5	-140.230463
dKK	0.03065	0.00019	161.742246
dKM	-0.017022	8.9E-5	-191.195646
cM	0.36652	0.000429	853.91927
dMq	-0.020719	6.2E-5	-332.487759
dML	-0.020466	7.4E-5	-276.229403
dMK	-0.017022	8.9E-5	-191.195646
dMM	0.037433	9.6E-5	389.133464

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) = 0.360969 + 4.0E-5 * ln(q) + 0.03435 * ln(wL) + -0.013792 * ln(wK) + -0.020466 * ln(wM),

sK(q;wL,wK,wM) = 0.272834 + 0.020684 * ln(q) + -0.013792 * ln(wL) + 0.03065 * ln(wK) + -0.017022 * ln(wM),

sM(q;wL,wK,wM) = 0.36652 + -0.020719 * ln(q) + -0.020466 * ln(wL) + -0.017022 * ln(wK) + 0.037433 * 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 = 0.360969 + 0.272834 + 0.36652 = 1.000323
0 =? dLL + dLK + dLM = 0.03435 + -0.013792 + -0.020466 = 9.2E-5
0 =? dKL + dKK + dKM = -0.013792 + 0.03065 + -0.017022 = -0.000164
0 =? dML + dMK + dMM = -0.020466 + -0.017022 + 0.037433 = -5.5E-5
0 =? dLq + dKq + dMq = 4.0E-5 + 0.020684 + -0.020719 = 5.0E-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
cL	0.361195	0.000175	2059.508127
dLq	2.9E-5	5.1E-5	0.571547
dLL	0.034295	8.7E-5	395.169601
dLK	-0.013811	7.2E-5	-191.017489
dLM	-0.020483	5.8E-5	-353.712248
cK	0.272423	0.000176	1543.50184
dKq	0.020689	5.1E-5	408.532369
dKL	-0.013811	7.2E-5	-191.017489
dKK	0.030782	8.6E-5	358.862583
dKM	-0.016971	5.7E-5	-295.271661
cM	0.366382	0.000177	2075.191371
dMq	-0.020718	5.0E-5	-412.87642
dML	-0.020483	5.8E-5	-353.712248
dMK	-0.016971	5.7E-5	-295.271661
dMM	0.037454	6.7E-5	561.557456

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) = 0.361195 + 2.9E-5 * ln(q) + 0.034295 * ln(wL) + -0.013811 * ln(wK) + -0.020483 * ln(wM),

sK(q;wL,wK,wM) = 0.272423 + 0.020689 * ln(q) + -0.013811 * ln(wL) + 0.030782 * ln(wK) + -0.016971 * ln(wM),

sM(q;wL,wK,wM) = 0.366382 + -0.020718 * ln(q) + -0.020483 * ln(wL) + -0.016971 * ln(wK) + 0.037454 * 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
c	1.115421	0.000143	7791.813873
cq	1	0	1000
dqq	0.02093	2.3E-5	891.215578

R2 = 1

R2b = 1

# obs = 31

1 = cq

VI. The Translog cost function as estimated is:

ln(C(q;wL,wK,wM)) = 1.115421 + 1 * ln(q) + 0.361195 * ln(wL) + 0.272423 * ln(wK) + 0.366382 * log(wM)
+ .5 * [0.02093 * ln(q)^² + 0.034295 * ln(wL)^² + 0.030782 * ln(wK)^² + 0.037454 * ln(wM)^²]
+ .5 * [-0.027622 * ln(wL)*ln(wK) + -0.040967 * ln(wL)*ln(wM) + -0.033941 * ln(wK)*log(wM)]
+ 2.9E-5 * ln(wL)*ln(q) + 0.020689 * ln(wK)*ln(q) + -0.020718 * ln(wM)*ln(q) (***)

VII. Check for linear homogeneity:

1 =? cL + cK + cM = 0.361195 + 0.272423 + 0.366382 = 1
0 =? dLL + dLK + dLM = 0.034295 + -0.013811 + -0.020483 = 0
0 =? dKL + dKK + dKM = -0.013811 + 0.030782 + -0.016971 = -0
0 =? dML + dMK + dMM = -0.020483 + -0.016971 + 0.037454 = -0
0 =? dLq + dKq + dMq = 2.9E-5 + 0.020689 + -0.020718 = -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) = 1412.92 =? 1412.92 = C(25; 14, 26,12).

2 * C(30; 7, 13, 6) = 1722.07 =? 1722.07 = 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 = 0.02093,

0 =? dLq = 2.9E-5, 0 =? dKq = 0.020689, 0 =? dMq = -0.020718.

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

C(30; 7, 13, 6) = 861.04 =? 722.45 = 30 * 24.08 = 30^^1/1 * C(1; 7, 13, 6).

Try it with rho = 0!

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 ∇²_wwC 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 ∇²_wwC is negative semidefinite if and only if the matrix:

H = W * ∇²_wwC * 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 * ∇²_wwC * W =

-0.19494	0.1159	0.07904
0.1159	-0.20085	0.08495
0.07904	0.08495	-0.16399

The principal minors of H are H1 = -0.194941, H2 = 0.02572, 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 = -0.3142, e2 = -0.2456, 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:

ε_L,wL = ∂ln(L(q;wL,wK,wM))/∂ln(wL) = -1 + sL(q;wL,wK,wM) + dLL / sL(q;wL,wK,wM),

ε_L,wK = ∂ln(L(q;wL,wK,wM))/∂ln(wK) = sK(q;wL,wK,wM) + dLK / sL(q;wL,wK,wM),

ε_L,wM = ∂ln(L(q;wL,wK,wM))/∂ln(wM) = sM(q;wL,wK,wM) + dLM / sL(q;wL,wK,wM),

ε_L,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:

ε_L,wL	ε_L,wK	ε_L,wM	ε_L,q
ε_K,wL	ε_K,wK	ε_K,wM	ε_K,q
ε_M,wL	ε_M,wK	ε_M,wM	ε_M,q

-0.5477	0.3257	0.2221	1.0873
0.318	-0.5511	0.2331	1.144
0.2827	0.3038	-0.5864	1.0131

XI. Uzawa Partial Elasticities of Substitution:

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

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

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

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

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

u_MK = 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 * K),

∂M(q;wL,wK,wM)/∂wK = (dMK * C / wK + sM * K) * C / (wM * M * L)

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

u_LK = u_KL, u_LM = u_ML, and u_KM = u_MK.

as seen in the following table.

XII. Table of Results: check that the estimated Translog cost function and input amounts for a given level of output agree with the Generalized CES production function's minimized cost and inputs. Also, compare the Allen partial elasticities of substitution, s_LK, s_LM, and s_MK, with the Uzawa partial elasticities of substitution, u_LK, u_LM, and u_KM.

Generalized CES-Translog Cost Function À1: Estimate the Translog cost function using restricted factor shares Parameters: nu = 1, rho = 0.17647, rhoL = 0.17647, rhoK = 0.11823, rhoM = 0.26339
					— Generalized CES Cost Data —								— Translog Cost Data —				Factor Shares			Uzawa Elasticities						W * ∇²_wwC * W
obs #	q	wL	wK	wM	L	K	M	s_LK	s_LM	s_KM	ε_LKM	cost	est cost	est L	est K	est M	sL	sK	sM	u_LK	u_LM	u_KL	u_KM	u_ML	u_MK	e₁	e₂	e₃
1	18	6.7	11.26	6.02	24.66	14.43	22.65	0.9	0.79	0.83	0.93	464.02	464.19	24.69	14.43	22.65	0.356	0.35	0.294	0.89	0.8	0.89	0.83	0.8	0.83	-0.307	-0.255	0
2	19	6.82	14.98	5.78	28.09	13.02	26.78	0.9	0.79	0.83	0.923	541.42	541.57	28.09	13.03	26.78	0.354	0.36	0.286	0.89	0.8	0.89	0.84	0.8	0.84	-0.311	-0.25	0
3	20	8	14.26	5.46	26.45	14.74	30.05	0.89	0.79	0.83	0.922	585.82	585.92	26.45	14.74	30.07	0.361	0.359	0.28	0.89	0.8	0.89	0.83	0.8	0.83	-0.314	-0.246	-0
4	21	7.88	12.5	5.44	26.94	16.67	30.29	0.9	0.79	0.83	0.924	585.4	585.46	26.93	16.67	30.31	0.363	0.356	0.282	0.89	0.8	0.89	0.83	0.8	0.83	-0.313	-0.247	0
5	22	8.06	11.18	7.82	29.35	20.57	25.06	0.9	0.79	0.83	0.932	662.54	662.57	29.38	20.56	25.05	0.357	0.347	0.296	0.89	0.81	0.89	0.83	0.81	0.83	-0.306	-0.256	0
6	23	6.6	14.06	4.76	33.02	15.87	35.37	0.89	0.79	0.83	0.918	609.39	609.38	33.01	15.87	35.38	0.358	0.366	0.276	0.89	0.79	0.89	0.83	0.79	0.83	-0.316	-0.243	0
7	24	7.98	13.2	6.54	32.78	19.74	31.75	0.9	0.79	0.83	0.924	729.89	729.85	32.78	19.74	31.75	0.358	0.357	0.285	0.89	0.8	0.89	0.83	0.8	0.83	-0.311	-0.249	0
8	25	6.72	13.58	6.36	37.75	19.15	32.31	0.9	0.79	0.83	0.923	719.27	719.21	37.75	19.15	32.31	0.353	0.362	0.286	0.89	0.8	0.89	0.84	0.8	0.84	-0.311	-0.249	0
9	26	8.86	13.92	7.72	35.66	22.59	32.71	0.9	0.79	0.83	0.925	882.87	882.77	35.66	22.58	32.71	0.358	0.356	0.286	0.89	0.8	0.89	0.83	0.8	0.83	-0.311	-0.25	0
10	27	8.6	12.22	4.78	32.58	22.47	42.93	0.89	0.79	0.83	0.918	760	759.86	32.56	22.47	42.94	0.368	0.361	0.27	0.9	0.79	0.9	0.83	0.79	0.83	-0.319	-0.24	0
11	28	6.76	11.64	7.14	41.57	24.45	32.4	0.9	0.79	0.83	0.926	797.01	796.89	41.57	24.45	32.39	0.353	0.357	0.29	0.89	0.8	0.89	0.84	0.8	0.84	-0.309	-0.252	-0
12	29	5.28	14.28	7.66	53.79	21.42	32.04	0.9	0.79	0.83	0.923	835.24	835.12	53.77	21.43	32.02	0.34	0.366	0.294	0.89	0.79	0.89	0.84	0.79	0.84	-0.308	-0.253	0
13	30	7	13	6	43.79	24.14	40.13	0.89	0.79	0.83	0.92	861.17	861.04	43.78	24.14	40.13	0.356	0.364	0.28	0.89	0.79	0.89	0.83	0.79	0.83	-0.314	-0.246	0
14	31	7.28	14.44	6.62	46.98	24.51	40.88	0.89	0.79	0.83	0.919	966.58	966.43	46.97	24.51	40.88	0.354	0.366	0.28	0.89	0.79	0.89	0.83	0.79	0.83	-0.314	-0.246	-0
15	32	7.72	13.48	5	43.33	25.23	49.6	0.89	0.79	0.83	0.915	922.53	922.37	43.31	25.23	49.6	0.362	0.369	0.269	0.9	0.79	0.9	0.83	0.79	0.83	-0.319	-0.238	0
16	33	8.88	12.96	6.98	44.07	30.15	43.23	0.89	0.79	0.83	0.921	1083.8	1083.64	44.05	30.14	43.24	0.361	0.36	0.279	0.89	0.8	0.89	0.83	0.8	0.83	-0.315	-0.245	0
17	34	6.16	11.38	5.3	50.14	27.97	45.38	0.89	0.79	0.83	0.918	867.66	867.54	50.12	27.96	45.39	0.356	0.367	0.277	0.89	0.79	0.89	0.83	0.79	0.83	-0.315	-0.244	0
18	35	8.86	11.84	7.14	45.92	34.06	44.04	0.89	0.79	0.83	0.922	1124.49	1124.35	45.9	34.05	44.05	0.362	0.359	0.28	0.89	0.8	0.89	0.83	0.8	0.83	-0.314	-0.246	0
19	36	6.1	14.94	4.48	56.68	24.73	57.66	0.89	0.79	0.83	0.91	973.43	973.31	56.68	24.72	57.65	0.355	0.379	0.265	0.9	0.78	0.9	0.83	0.78	0.83	-0.322	-0.235	0
20	37	7.46	12.54	5.18	50.89	30.91	54.52	0.89	0.79	0.83	0.915	1049.68	1049.58	50.87	30.91	54.52	0.362	0.369	0.269	0.9	0.79	0.9	0.83	0.79	0.83	-0.319	-0.239	0
21	38	7.76	13.92	4.84	52.34	30.04	60.93	0.89	0.79	0.83	0.912	1119.27	1119.19	52.34	30.04	60.91	0.363	0.374	0.263	0.9	0.79	0.9	0.83	0.79	0.83	-0.322	-0.235	0
22	39	5.5	11.06	5.9	62.86	32.89	47.04	0.89	0.79	0.83	0.919	986.97	986.95	62.84	32.89	47.05	0.35	0.369	0.281	0.89	0.79	0.89	0.84	0.79	0.84	-0.314	-0.246	-0
23	40	8.1	12.96	6.96	57.08	36.45	51.26	0.89	0.79	0.83	0.918	1291.49	1291.48	57.07	36.45	51.27	0.358	0.366	0.276	0.89	0.79	0.89	0.83	0.79	0.83	-0.316	-0.243	0
24	41	5.18	11.78	5.24	68.26	32.13	53.21	0.89	0.79	0.83	0.915	1010.92	1010.93	68.25	32.13	53.23	0.35	0.374	0.276	0.89	0.79	0.89	0.84	0.79	0.84	-0.316	-0.242	0
25	42	5.88	11.28	7.08	68.85	37.75	46.73	0.89	0.79	0.83	0.92	1161.46	1161.52	68.84	37.76	46.73	0.349	0.367	0.285	0.89	0.79	0.89	0.84	0.79	0.84	-0.312	-0.248	0
26	43	5.56	12.16	5.92	71.77	35.08	53.54	0.89	0.79	0.83	0.915	1142.56	1142.65	71.77	35.07	53.57	0.349	0.373	0.278	0.89	0.79	0.89	0.84	0.79	0.84	-0.316	-0.243	-0
27	44	6.06	13.72	7.88	77.83	37.03	49.29	0.89	0.79	0.83	0.918	1368.12	1368.3	77.82	37.04	49.31	0.345	0.371	0.284	0.89	0.79	0.89	0.84	0.79	0.84	-0.313	-0.247	0
28	45	5.94	14.82	4.02	71.68	31.14	76.56	0.89	0.79	0.83	0.905	1195.03	1195.16	71.77	31.12	76.52	0.357	0.386	0.257	0.9	0.78	0.9	0.83	0.78	0.83	-0.325	-0.23	0
29	46	8.54	12.84	4.7	59.29	40.11	75.57	0.89	0.79	0.83	0.91	1376.6	1376.86	59.32	40.14	75.51	0.368	0.374	0.258	0.9	0.78	0.9	0.82	0.78	0.82	-0.325	-0.231	0
30	47	6.82	14.26	4.94	71.78	36.52	72.64	0.89	0.79	0.83	0.908	1369.2	1369.47	71.83	36.52	72.64	0.358	0.38	0.262	0.9	0.78	0.9	0.83	0.78	0.83	-0.323	-0.233	0
31	48	6.88	11.08	7.24	72.94	46.91	54.86	0.89	0.79	0.83	0.919	1418.74	1419.1	72.95	46.92	54.88	0.354	0.366	0.28	0.89	0.79	0.89	0.83	0.79	0.83	-0.314	-0.246	0
AVE:	33	7.09	12.95	6.03	49	27.19	44.88	0.89	0.79	0.83	0.919	950.41	950.41	49	27.19	44.88	0.356	0.366	0.278	0.89	0.79	0.89	0.83	0.79	0.83	-0.315	-0.244	0

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 "Generalized CES-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 nu, rho, rhoL, rhoK, rhoM, alpha, beta and gamma of the Generalized CES production function used to generate these data. We impose 5 restrictions on the estimates of the parameters as specified:

SVD Restricted Least Squares
Parameter Estimates
Parameter	Coefficient	std error	t-ratio
c	1.092611	0.000333	3276.936868
cq	1.013514	0.000191	5312.334766
cL	0.361687	0.000215	1682.205769
cK	0.272084	0.000158	1717.157702
cM	0.366229	0.000143	2567.289384
dqq	0.016959	5.5E-5	306.521265
dLL	0.034384	0.000129	266.826302
dKK	0.030949	0.000133	232.867959
dMM	0.037485	0.000119	314.604428
2*dLK	-0.027848	0.000294	-94.570877
2*dLM	-0.040919	0.000138	-296.232723
2*dKM	-0.03405	0.000165	-206.873158
dLq	-0.000167	0.000104	-1.606658
dKq	0.041519	9.2E-5	449.711482
dMq	-0.041352	7.4E-5	-559.846927

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

XIV. The Translog cost function estimated directly is:

ln(C(q;wL,wK,wM)) = 1.092611 + 1.013514 * ln(q) + 0.361687 * ln(wL) + 0.272084 * ln(wK) + 0.366229 * log(wM)
+ .5 * [0.016959 * ln(q)^² + 0.034384 * ln(wL)^² + 0.030949 * ln(wK)^² + 0.037485 * ln(wM)^²]
+ .5 * [-0.027848 * ln(wL)*ln(wK) + -0.040919 * ln(wL)*ln(wM) + -0.03405 * ln(wK)*log(wM)]
+ -0.000167 * ln(wL)*ln(q) + 0.041519 * ln(wK)*ln(q) + -0.041352 * ln(wM)*ln(q) (***)

XV. Its three derived factor share functions are:

sL(q;wL,wK,wM) = 0.361687 + -0.000167 * ln(q) + 0.034384 * ln(wL) + -0.013924 * ln(wK) + -0.02046 * ln(wM),

sK(q;wL,wK,wM) = 0.272084 + 0.041519 * ln(q) + -0.013924 * ln(wL) + 0.030949 * ln(wK) + -0.017025 * ln(wM),

sM(q;wL,wK,wM) = 0.366229 + -0.041352 * ln(q) + -204.6 * ln(wL) + -0.017025 * ln(wK) + 0.037485 * 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 = 0.361687 + 0.272084 + 0.366229 = 1
0 =? dLL + dLK + dLM = 0.034384 + -0.013924 + -0.02046 = 0
0 =? dKL + dKK + dKM = -0.013924 + 0.030949 + -0.017025 = 0
0 =? dML + dMK + dMM = -0.02046 + -0.017025 + 0.037485 = 0
0 =? dLq + dKq + dMq = -0.000167 + 0.041519 + -0.041352 = 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 ∇²_wwC 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 ∇²_wwC is negative semidefinite if and only if the matrix:

H = W * ∇²_wwC * 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 * ∇²_wwC * W =

-0.19478	0.14081	0.05397
0.14081	-0.21484	0.07402
0.05397	0.07402	-0.12799

The principal minors of H are H1 = -0.194781, H2 = 0.022017, 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 = -0.3476, e2 = -0.19, 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) = 1487.91 =? 1487.91 = C(25; 14, 26,12).

2 * C(30; 7, 13, 6) = 1818.92 =? 1818.92 = 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 = 1.013514, 0 =? dqq = 0.016959,

0 =? dLq = -0.000167, 0 =? dKq = 0.041519, 0 =? dMq = -0.041352.

As examples:

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

C(25; 7, 13, 6) = 743.96 =? 588.38 = 25 * 23.54 = 25^^1/1 * C(1; 7, 13, 6).

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

C(30; 7, 13, 6) = 909.46 =? 706.06 = 30 * 23.54 = 30^^1/1 * C(1; 7, 13, 6).

Try it with rho = 0!

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:

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

ε_L,wL	ε_L,wK	ε_L,wM	ε_L,q
ε_K,wL	ε_K,wK	ε_K,wM	ε_K,q
ε_M,wL	ε_M,wK	ε_M,wM	ε_M,q

-0.5477	0.3959	0.1517	1.1028
0.3237	-0.4938	0.1701	1.1987
0.2579	0.3537	-0.6116	0.9057

XX. Table of Results: check that the estimated Translog cost function and input amounts for a given level of output agree with the Generalized CES production function's minimized cost and inputs. Also, compare the Allen partial elasticities of substitution, s_LK, s_LM, and s_MK, with the Uzawa partial elasticities of substitution, u_LK, u_LM, and u_KM. Comparing the estimated cost and input amounts obtained from the Translog cost function with the Generalized CES data, one can conclude that method À1 obtains more accurate results than method À2 for rho > 0.

Generalized CES-Translog Cost Function À2: Estimate the Translog cost function directly Parameters: nu = 1, rho = 0.17647, rhoL = 0.17647, rhoK = 0.11823, rhoM = 0.26339
				— Generalized CES Cost Data —								— Translog Cost Data —				Factor Shares			Uzawa Elasticities						W * ∇²_wwC * W
obs #	q	wL	wK	wM	L	K	M	s_LK	s_LM	s_KM	ε_LKM	cost	est cost	est L	est K	est M	sL	sK	sM	u_LK	u_LM	u_KL	u_KM	u_ML	u_MK	e₁	e₂	e₃
1	18	6.7	11.26	6.02	24.66	14.43	22.65	0.9	0.79	0.83	0.93	464.02	481.77	25.61	17.54	18.72	0.356	0.41	0.234	0.9	0.75	0.9	0.82	0.75	0.82	-0.337	-0.211	0
2	19	6.82	14.98	5.78	28.09	13.02	26.78	0.9	0.79	0.83	0.923	541.42	573.85	29.75	16.15	22.33	0.354	0.422	0.225	0.91	0.74	0.91	0.82	0.74	0.82	-0.341	-0.203	0
3	20	8	14.26	5.46	26.45	14.74	30.05	0.89	0.79	0.83	0.922	585.82	621.8	28.06	18.35	24.85	0.361	0.421	0.218	0.91	0.74	0.91	0.81	0.74	0.81	-0.344	-0.198	-0
4	21	7.88	12.5	5.44	26.94	16.67	30.29	0.9	0.79	0.83	0.924	585.4	616.95	28.37	20.68	24.79	0.362	0.419	0.219	0.91	0.74	0.91	0.81	0.74	0.81	-0.344	-0.199	-0
5	22	8.06	11.18	7.82	29.35	20.57	25.06	0.9	0.79	0.83	0.932	662.54	677.91	30.05	24.93	20.09	0.357	0.411	0.232	0.91	0.75	0.91	0.82	0.75	0.82	-0.338	-0.209	0
6	23	6.6	14.06	4.76	33.02	15.87	35.37	0.89	0.79	0.83	0.918	609.39	653.85	35.4	20.05	29.04	0.357	0.431	0.211	0.91	0.73	0.91	0.81	0.73	0.81	-0.347	-0.192	0
7	24	7.98	13.2	6.54	32.78	19.74	31.75	0.9	0.79	0.83	0.924	729.89	764.46	34.32	24.5	25.57	0.358	0.423	0.219	0.91	0.74	0.91	0.82	0.74	0.82	-0.344	-0.199	-0
8	25	6.72	13.58	6.36	37.75	19.15	32.31	0.9	0.79	0.83	0.923	719.27	756.65	39.69	23.87	26.06	0.352	0.428	0.219	0.91	0.74	0.91	0.82	0.74	0.82	-0.343	-0.198	-0
9	26	8.86	13.92	7.72	35.66	22.59	32.71	0.9	0.79	0.83	0.925	882.87	918.76	37.09	27.96	26.02	0.358	0.424	0.219	0.91	0.74	0.91	0.82	0.74	0.82	-0.344	-0.198	-0
10	27	8.6	12.22	4.78	32.58	22.47	42.93	0.89	0.79	0.83	0.918	760	810.26	34.7	28.5	34.23	0.368	0.43	0.202	0.91	0.72	0.91	0.8	0.72	0.8	-0.352	-0.184	0
11	28	6.76	11.64	7.14	41.57	24.45	32.4	0.9	0.79	0.83	0.926	797.01	824.42	42.98	30.19	25.54	0.352	0.426	0.221	0.91	0.74	0.91	0.82	0.74	0.82	-0.342	-0.2	-0
12	29	5.28	14.28	7.66	53.79	21.42	32.04	0.9	0.79	0.83	0.923	835.24	872.5	56.13	26.66	25.51	0.34	0.436	0.224	0.91	0.73	0.91	0.83	0.73	0.83	-0.34	-0.201	0
13	30	7	13	6	43.79	24.14	40.13	0.89	0.79	0.83	0.92	861.17	909.46	46.21	30.44	31.72	0.356	0.435	0.209	0.91	0.73	0.91	0.81	0.73	0.81	-0.348	-0.19	0
14	31	7.28	14.44	6.62	46.98	24.51	40.88	0.89	0.79	0.83	0.919	966.58	1021.82	49.62	30.96	32.26	0.354	0.437	0.209	0.91	0.72	0.91	0.81	0.72	0.81	-0.348	-0.19	0
15	32	7.72	13.48	5	43.33	25.23	49.6	0.89	0.79	0.83	0.915	922.53	990.64	46.48	32.38	39.06	0.362	0.441	0.197	0.91	0.71	0.91	0.8	0.71	0.8	-0.353	-0.18	0
16	33	8.88	12.96	6.98	44.07	30.15	43.23	0.89	0.79	0.83	0.921	1083.8	1133.51	46.05	37.88	33.48	0.361	0.433	0.206	0.91	0.72	0.91	0.81	0.72	0.81	-0.349	-0.188	0
17	34	6.16	11.38	5.3	50.14	27.97	45.38	0.89	0.79	0.83	0.918	867.66	917.54	52.97	35.48	35.37	0.356	0.44	0.204	0.91	0.72	0.91	0.81	0.72	0.81	-0.35	-0.186	0
18	35	8.86	11.84	7.14	45.92	34.06	44.04	0.89	0.79	0.83	0.922	1124.49	1167.19	47.62	42.62	33.7	0.361	0.432	0.206	0.91	0.73	0.91	0.81	0.73	0.81	-0.349	-0.188	0
19	36	6.1	14.94	4.48	56.68	24.73	57.66	0.89	0.79	0.83	0.91	973.43	1064.58	61.94	32.34	45.44	0.355	0.454	0.191	0.91	0.7	0.91	0.8	0.7	0.8	-0.355	-0.174	0
20	37	7.46	12.54	5.18	50.89	30.91	54.52	0.89	0.79	0.83	0.915	1049.68	1121.47	54.32	39.73	42.09	0.361	0.444	0.194	0.91	0.71	0.91	0.8	0.71	0.8	-0.354	-0.177	0
21	38	7.76	13.92	4.84	52.34	30.04	60.93	0.89	0.79	0.83	0.912	1119.27	1212.05	56.64	39.12	47.12	0.363	0.449	0.188	0.91	0.7	0.91	0.8	0.7	0.8	-0.357	-0.171	0
22	39	5.5	11.06	5.9	62.86	32.89	47.04	0.89	0.79	0.83	0.919	986.97	1035.31	65.87	41.62	36.06	0.35	0.445	0.205	0.91	0.72	0.91	0.81	0.72	0.81	-0.349	-0.186	0
23	40	8.1	12.96	6.96	57.08	36.45	51.26	0.89	0.79	0.83	0.918	1291.49	1354.4	59.8	46.23	38.92	0.358	0.442	0.2	0.91	0.71	0.91	0.81	0.71	0.81	-0.352	-0.182	0
24	41	5.18	11.78	5.24	68.26	32.13	53.21	0.89	0.79	0.83	0.915	1010.92	1076.07	72.58	41.24	40.89	0.349	0.451	0.199	0.91	0.71	0.91	0.81	0.71	0.81	-0.351	-0.18	0
25	42	5.88	11.28	7.08	68.85	37.75	46.73	0.89	0.79	0.83	0.92	1161.46	1204.27	71.31	47.43	35.3	0.348	0.444	0.208	0.91	0.72	0.91	0.82	0.72	0.82	-0.348	-0.188	0
26	43	5.56	12.16	5.92	71.77	35.08	53.54	0.89	0.79	0.83	0.915	1142.56	1208.64	75.84	44.87	40.78	0.349	0.451	0.2	0.91	0.71	0.91	0.81	0.71	0.81	-0.351	-0.181	0
27	44	6.06	13.72	7.88	77.83	37.03	49.29	0.89	0.79	0.83	0.918	1368.12	1429.17	81.2	46.88	37.3	0.344	0.45	0.206	0.91	0.71	0.91	0.82	0.71	0.82	-0.348	-0.186	0
28	45	5.94	14.82	4.02	71.68	31.14	76.56	0.89	0.79	0.83	0.905	1195.03	1324.64	79.46	41.57	58.86	0.356	0.465	0.179	0.92	0.68	0.92	0.8	0.68	0.8	-0.36	-0.162	0
29	46	8.54	12.84	4.7	59.29	40.11	75.57	0.89	0.79	0.83	0.91	1376.6	1490.81	64.17	52.69	56.64	0.368	0.454	0.179	0.92	0.69	0.92	0.79	0.69	0.79	-0.362	-0.162	0
30	47	6.82	14.26	4.94	71.78	36.52	72.64	0.89	0.79	0.83	0.908	1369.2	1490.1	78.08	48.09	55.01	0.357	0.46	0.182	0.92	0.69	0.92	0.8	0.69	0.8	-0.359	-0.166	0
31	48	6.88	11.08	7.24	72.94	46.91	54.86	0.89	0.79	0.83	0.919	1418.74	1468.11	75.4	59.19	40.54	0.353	0.447	0.2	0.91	0.71	0.91	0.81	0.71	0.81	-0.351	-0.182	0
AVE:	33	7.09	12.95	6.03	51.86	34.52	34.95	0.89	0.79	0.83	0.919	950.41	1006.22	51.86	34.52	34.95	0.356	0.437	0.207	0.91	0.72	0.91	0.81	0.72	0.81	-0.349	-0.187	0

Mathematical Notes

1. The Translog Cost Function:

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).

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
ε_L,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
ε_L,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
ε_L,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