www.egwald.com Egwald Web Services

Egwald Web Services
Domain Names
Web Site Design

Egwald Website Search Twitter - Follow Elmer Wiens Radio Podcasts - Geraldos Hour

 

Statistics Programs - Econometrics and Probability Economics - Microeconomics & Macroeconomics Operations Research - Linear Programming and Game Theory Egwald's Mathematics Egwald's Optimal Control
Egwald HomeEconomics Home PageOligopoly/Public Firm ModelRun Oligopoly ModelDerive Oligopoly ModelProduction FunctionsCost FunctionsDuality Production Cost FunctionsGraduate EssaysReferences & Links
 

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: Twitter - Follow Elmer Wiens

Cost Functions:  Cobb-Douglas Cost | Normalized Quadratic Cost | Translog Cost | Diewert Cost | Generalized CES-Translog Cost | Generalized CES-Diewert Cost | References and Links

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:

εLKM = 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
cL0.3608860.001399.912
dLq7.1E-500.853
dLL0.0342650197.171
dLK-0.0136470-47.613
dLM-0.0205840-160.098
R2 = 0.9997 R2b = 0.9997 # obs = 31

QR Least Squares
Parameter Estimates
Parameter Coefficient std error t-ratio
cK0.2722750.001412.782
dKq0.0207420341.731
dKL-0.0138770-109.244
dKK0.030750146.772
dKM-0.016890-179.724
R2 = 0.9998 R2b = 0.9998 # obs = 31

QR Least Squares
Parameter Estimates
Parameter Coefficient std error t-ratio
cM0.3668380.001419.68
dMq-0.0208130-258.759
dML-0.0203890-121.122
dMK-0.0171030-61.602
dMM0.0374750300.91
R2 = 0.9999 R2b = 0.9998 # obs = 31

The three estimated factor share functions are:

sL(q;wL,wK,wM) = 0.360886 + 7.1E-5 * ln(q) + 0.034265 * ln(wL) + -0.013647 * ln(wK) + -0.020584 * ln(wM),

sK(q;wL,wK,wM) = 0.272275 + 0.020742 * ln(q) + -0.013877 * ln(wL) + 0.03075 * ln(wK) + -0.01689 * ln(wM),

sM(q;wL,wK,wM) = 0.366838 + -0.020813 * ln(q) + -0.020389 * ln(wL) + -0.017103 * ln(wK) + 0.037475 * 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   =   0.360886 + 0.272275 + 0.366838   =   1
0   =?   dLL + dLK + dLM   =   0.034265 + -0.013647 + -0.020584  =   3.4E-5
0   =?   dKL + dKK + dKM   =   -0.013877 + 0.03075 + -0.01689  =   -1.7E-5
0   =?   dML + dMK + dMM   =   -0.020389 + -0.017103 + 0.037475  =   -1.7E-5
0   =?   dLq + dKq + dMq   =   7.1E-5 + 0.020742 + -0.020813  =   -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
cL0.3611970.000493733.22975
dLq6.9E-56.5E-51.05496
dLL0.0342830.000134256.033068
dLK-0.0138320.000116-119.326166
dLM-0.0205058.1E-5-254.323853
cK0.272180.000681399.743844
dKq0.0207436.5E-5318.317434
dKL-0.0138320.000116-119.326166
dKK0.0307720.000224137.182416
dKM-0.0169199.1E-5-185.556595
cM0.3666550.000398922.186989
dMq-0.0208136.5E-5-319.339228
dML-0.0205058.1E-5-254.323853
dMK-0.0169199.1E-5-185.556595
dMM0.0374359.7E-5385.119992
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) = 0.361197 + 6.9E-5 * ln(q) + 0.034283 * ln(wL) + -0.013832 * ln(wK) + -0.020505 * ln(wM),

sK(q;wL,wK,wM) = 0.27218 + 0.020743 * ln(q) + -0.013832 * ln(wL) + 0.030772 * ln(wK) + -0.016919 * ln(wM),

sM(q;wL,wK,wM) = 0.366655 + -0.020813 * ln(q) + -0.020505 * ln(wL) + -0.016919 * ln(wK) + 0.037435 * 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   =   0.361197 + 0.27218 + 0.366655   =   1.000031
0   =?   dLL + dLK + dLM   =   0.034283 + -0.013832 + -0.020505  =   -5.4E-5
0   =?   dKL + dKK + dKM   =   -0.013832 + 0.030772 + -0.016919  =   2.1E-5
0   =?   dML + dMK + dMM   =   -0.020505 + -0.016919 + 0.037435  =   1.2E-5
0   =?   dLq + dKq + dMq   =   6.9E-5 + 0.020743 + -0.020813  =   -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
cL0.3610870.0001941860.323335
dLq6.9E-55.3E-51.293846
dLL0.0343187.9E-5435.817962
dLK-0.013828.1E-5-170.208068
dLM-0.0204985.6E-5-363.602071
cK0.2722320.0002071315.7065
dKq0.0207445.3E-5389.605274
dKL-0.013828.1E-5-170.208068
dKK0.0307480.000119257.523513
dKM-0.0169287.3E-5-231.350696
cM0.3666820.0001951877.578001
dMq-0.0208135.3E-5-390.812918
dML-0.0204985.6E-5-363.602071
dMK-0.0169287.3E-5-231.350696
dMM0.0374267.1E-5527.012227
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) = 0.361087 + 6.9E-5 * ln(q) + 0.034318 * ln(wL) + -0.01382 * ln(wK) + -0.020498 * ln(wM),

sK(q;wL,wK,wM) = 0.272232 + 0.020744 * ln(q) + -0.01382 * ln(wL) + 0.030748 * ln(wK) + -0.016928 * ln(wM),

sM(q;wL,wK,wM) = 0.366682 + -0.020813 * ln(q) + -0.020498 * ln(wL) + -0.016928 * ln(wK) + 0.037426 * 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
c1.1155380.0001388104.006653
cq101000
dqq0.0209122.3E-5926.049288
R2 = 1 R2b = 1 # obs = 31

1 = cq

VI. The Translog cost function as estimated is:

ln(C(q;wL,wK,wM)) = 1.115538 + 1 * ln(q) + 0.361087 * ln(wL) + 0.272232 * ln(wK) + 0.366682 * log(wM)
+ .5 * [0.020912 * ln(q)^2 + 0.034318 * ln(wL)^2 + 0.030748 * ln(wK)^2 + 0.037426 * ln(wM)^2]
+ .5 * [-0.02764 * ln(wL)*ln(wK) + -0.040996 * ln(wL)*ln(wM) + -0.033857 * ln(wK)*log(wM)]
+ 6.9E-5 * ln(wL)*ln(q) + 0.020744 * ln(wK)*ln(q) + -0.020813 * ln(wM)*ln(q)           (***)

VII. Check for linear homogeneity:

1   =?   cL + cK + cM   =   0.361087 + 0.272232 + 0.366682   =   1
0   =?   dLL + dLK + dLM   =   0.034318 + -0.01382 + -0.020498  =   0
0   =?   dKL + dKK + dKM   =   -0.01382 + 0.030748 + -0.016928  =   0
0   =?   dML + dMK + dMM   =   -0.020498 + -0.016928 + 0.037426  =   0
0   =?   dLq + dKq + dMq   =   6.9E-5 + 0.020744 + -0.020813  =   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) = 1412.93 =? 1412.93 = C(25; 14, 26,12).

2 * C(30; 7, 13, 6) = 1722.08 =? 1722.08 = 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  =   0.020912,

0   =?   dLq  =   6.9E-5,     0   =?   dKq  =   0.020744,     0   =?   dMq  =   -0.020813.

      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) = 861.04 =? 722.41 = 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 ∇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 =
dLLdLKdLM
dKLdKKdKM
dMLdMKdMM
S =
sL 0 0
 0sK 0
 0 0sM
SS =
sL*sLsL*sKsL*sM
sK*sLsK*sKsK*sM
sM*sLsM*sKsM*sM
W =
wL 0 0
 0wK 0
 0 0wM

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 =
-0.194920.115890.07903
0.11589-0.200870.08498
0.079030.08498-0.16402

The principal minors of H are H1 = -0.194925, H2 = 0.025725, 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:

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,wLεL,wKεL,wMεL,q
εK,wLεK,wKεK,wMεK,q
εM,wLεM,wKεM,wMεM,q
-0.54770.32560.22211.0874
0.318-0.55120.23321.1441
0.28260.3039-0.58651.0128

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 * 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:

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

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, sLK, sLM, and sMK, with the Uzawa partial elasticities of substitution, uLK, uLM, and uKM.

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 SharesUzawa ElasticitiesW * ∇2wwC * W
obs #qwLwKwM LK MsLKsLMsKMεLKMcostest costest Lest Kest MsLsKsMuLKuLMuKLuKMuMLuMKe1e2e3
1186.86 11.287.42 25.5915.3 20.240.90.790.840.934498.27498.4425.6315.320.220.3530.3460.3010.890.810.890.840.810.84 -0.303-0.2590
2197.06 13.125.36 25.9713.92 27.150.90.790.830.924511.55511.6825.9713.9227.170.3580.3570.2850.890.80.890.830.80.83 -0.311-0.2490
3205.82 14.945.18 31.5912.82 28.730.890.790.830.921524.15524.2531.5912.8228.740.3510.3650.2840.890.790.890.840.790.84 -0.312-0.2480
4216.94 11.784.64 27.3315.93 31.490.890.790.830.922523.47523.5227.3315.9331.510.3620.3580.2790.890.80.890.830.80.83 -0.314-0.246-0
5226.08 14.54.88 33.4214.53 32.870.890.790.830.919574.25574.2733.4214.5332.880.3540.3670.2790.890.790.890.830.790.83 -0.314-0.245-0
6236.68 12.526.68 34.0918.4 28.130.90.790.830.926646.07646.0634.118.428.120.3530.3570.2910.890.80.890.840.80.84 -0.309-0.2530
7247.8 12.464.88 30.5418.9 36.80.890.790.830.92653.31653.2630.5218.936.820.3640.360.2750.890.80.890.830.80.83 -0.316-0.2430
8255.22 14.827.14 45.8917.35 28.960.90.790.830.923703.44703.3845.8817.3628.940.340.3660.2940.890.80.890.840.80.84 -0.308-0.253-0
9265.66 13.65.92 42.6318.64 33.430.890.790.830.921692.75692.6842.6218.6433.430.3480.3660.2860.890.790.890.840.790.84 -0.312-0.2490
10277.36 12.264.88 35.6321.42 40.580.890.790.830.919722.84722.7335.6121.4140.590.3630.3630.2740.90.790.90.830.790.83 -0.317-0.2420
11288.22 144.1 35.1520.7 50.20.890.790.830.913784.64784.535.1420.7150.190.3680.370.2620.90.790.90.830.790.83 -0.323-0.2340
12296.8 12.926.54 43.6323.56 36.510.890.790.830.922839.88839.7543.6223.5636.50.3530.3620.2840.890.80.890.840.80.84 -0.312-0.2490
13307 136 43.7924.14 40.130.890.790.830.92861.17861.0443.7824.1440.130.3560.3640.280.890.790.890.830.790.83 -0.314-0.246-0
14317.1 12.485 42.724.7 45.80.890.790.830.917840.37840.2342.6924.6945.80.3610.3670.2730.90.790.90.830.790.83 -0.318-0.2410
15327.98 13.56.76 45.4527.29 41.930.890.790.830.921014.61014.4545.4427.2841.930.3570.3630.2790.890.790.890.830.790.83 -0.314-0.246-0
16336.7 11.444.66 45.1626.88 48.720.890.790.830.916837.17837.0445.1526.8848.730.3610.3670.2710.90.790.90.830.790.83 -0.318-0.240
17345.32 12.746.58 59.2126.42 39.760.890.790.830.92913.15913.0459.1926.4239.750.3450.3690.2860.890.790.890.840.790.84 -0.311-0.249-0
18357.44 12.564.34 46.2327.83 57.230.890.790.830.913941.9941.7846.2227.8357.210.3650.3710.2640.90.790.90.830.790.83 -0.322-0.2350
19366.68 12.64.72 51.528.24 54.380.890.790.830.914956.44956.3351.4928.2354.370.360.3720.2680.90.790.90.830.790.83 -0.32-0.2380
20375.6 114 53.9128.57 56.260.890.790.830.913841.25841.1853.9128.5756.260.3590.3740.2680.90.790.90.830.790.83 -0.32-0.2370
21385.12 11.446.14 64.8630.93 44.350.890.790.830.919958.3958.2564.8530.9444.350.3470.3690.2840.890.790.890.840.790.84 -0.312-0.248-0
22395.84 12.566.22 64.1731.65 48.230.890.790.830.9171072.191072.1564.1631.6448.230.3490.3710.280.890.790.890.840.790.84 -0.314-0.245-0
23405.6 14.746.16 71.0729.42 51.710.890.790.830.9141150.151150.1371.0629.4151.740.3460.3770.2770.890.790.890.840.790.84 -0.316-0.243-0
24418.6 13.266.32 5636.93 56.990.890.790.830.9161331.51331.515636.93570.3620.3680.2710.90.790.90.830.790.83 -0.319-0.240
25428.84 11.224.12 49.0438.23 72.230.890.790.830.9111160.041160.1149.0438.2672.160.3740.370.2560.90.790.90.820.790.82 -0.326-0.23-0
26437.4 14.924.44 61.8632.26 73.410.890.790.830.9071265.011265.1361.9132.2673.350.3620.380.2570.90.780.90.830.780.83 -0.325-0.230
27446.02 12.747.4 75.1537.91 49.880.890.790.830.9181304.551304.6975.1537.9249.890.3470.370.2830.890.790.890.840.790.84 -0.313-0.247-0
28456.22 13.826.64 75.8736.66 56.270.890.790.830.9151352.231352.4175.8836.6656.290.3490.3750.2760.890.790.890.840.790.84 -0.316-0.2430
29467.36 13.27.52 71.6841.83 55.250.890.790.830.9171495.141495.471.6941.8355.260.3530.3690.2780.890.790.890.840.790.84 -0.315-0.2440
30476.04 13.444.48 73.6835.5 73.040.890.790.830.9081249.451249.6873.7435.573.040.3560.3820.2620.90.780.90.830.780.83 -0.323-0.2330
31486.16 13.266.88 81.3640.59 57.940.890.790.830.9151438.011438.3581.3740.5957.970.3480.3740.2770.890.790.890.840.790.84 -0.316-0.2430
AVE:336.69 12.975.68 49.8126.37 45.760.890.790.830.918924.43924.4349.8126.3745.760.3560.3670.2770.890.790.890.830.790.83-0.316-0.2430




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
c1.0935070.0003433186.408919
cq1.013170.0002254511.688368
cL0.3619880.0003151147.474001
cK0.2713810.000319849.523364
cM0.3666320.0001183098.267652
dqq0.0170296.6E-5257.31199
dLL0.034490.000142242.183728
dKK0.0312660.00031399.924255
dMM0.0374940.000102367.360692
2*dLK-0.0282620.000405-69.791165
2*dLM-0.0407180.000175-232.431707
2*dKM-0.034270.00028-122.540996
dLq-0.0002360.000113-2.089562
dKq0.0417579.8E-5423.998256
dMq-0.0415217.3E-5-567.542632
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)) = 1.093507 + 1.01317 * ln(q) + 0.361988 * ln(wL) + 0.271381 * ln(wK) + 0.366632 * log(wM)
+ .5 * [0.017029 * ln(q)^2 + 0.03449 * ln(wL)^2 + 0.031266 * ln(wK)^2 + 0.037494 * ln(wM)^2]
+ .5 * [-0.028262 * ln(wL)*ln(wK) + -0.040718 * ln(wL)*ln(wM) + -0.03427 * ln(wK)*log(wM)]
+ -0.000236 * ln(wL)*ln(q) + 0.041757 * ln(wK)*ln(q) + -0.041521 * ln(wM)*ln(q)           (***)

XV. Its three derived factor share functions are:

sL(q;wL,wK,wM) = 0.361988 + -0.000236 * ln(q) + 0.03449 * ln(wL) + -0.014131 * ln(wK) + -0.020359 * ln(wM),

sK(q;wL,wK,wM) = 0.271381 + 0.041757 * ln(q) + -0.014131 * ln(wL) + 0.031266 * ln(wK) + -0.017135 * ln(wM),

sM(q;wL,wK,wM) = 0.366632 + -0.041521 * ln(q) + -203.59 * ln(wL) + -0.017135 * ln(wK) + 0.037494 * 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   =   0.361988 + 0.271381 + 0.366632   =   1
0   =?   dLL + dLK + dLM   =   0.03449 + -0.014131 + -0.020359  =   0
0   =?   dKL + dKK + dKM   =   -0.014131 + 0.031266 + -0.017135  =   0
0   =?   dML + dMK + dMM   =   -0.020359 + -0.017135 + 0.037494  =   0
0   =?   dLq + dKq + dMq   =   -0.000236 + 0.041757 + -0.041521  =   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 =
dLLdLKdLM
dKLdKKdKM
dMLdMKdMM
S =
sL 0 0
 0sK 0
 0 0sM
SS =
sL*sLsL*sKsL*sM
sK*sLsK*sKsK*sM
sM*sLsM*sKsM*sM
W =
wL 0 0
 0wK 0
 0 0wM

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 =
-0.194650.140690.05396
0.14069-0.214560.07387
0.053960.07387-0.12784

The principal minors of H are H1 = -0.194652, H2 = 0.021972, 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 = -0.3472, e2 = -0.1898, 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) = 1488.34 =? 1488.34 = C(25; 14, 26,12).

2 * C(30; 7, 13, 6) = 1819.46 =? 1819.46 = 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   =   1.01317,     0   =?   dqq  =   0.017029,

0   =?   dLq  =   -0.000236,     0   =?   dKq  =   0.041757,     0   =?   dMq  =   -0.041521.

      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) = 744.17 =? 588.66 = 25 * 23.55 = 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) = 909.73 =? 706.39 = 30 * 23.55 = 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:

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,wLεL,wKεL,wMεL,q
εK,wLεK,wKεK,wMεK,q
εM,wLεM,wKεM,wMεM,q
-0.54740.39570.15181.1027
0.3231-0.49280.16971.1992
0.25820.3534-0.61160.9047

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, sLK, sLM, and sMK, with the Uzawa partial elasticities of substitution, uLK, uLM, and uKM. 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 and rho < 0. Try it with 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 SharesUzawa ElasticitiesW * ∇2wwC * W
obs #qwLwKwM LK MsLKsLMsKMεLKMcostest costest Lest Kest MsLsKsMuLKuLMuKLuKMuMLuMKe1e2e3
1186.86 11.287.42 25.5915.3 20.240.90.790.840.934498.27511.0426.2718.4116.60.3530.4060.2410.90.760.90.830.760.83 -0.333-0.2160
2197.06 13.125.36 25.9713.92 27.150.90.790.830.924511.55540.4427.4217.2422.530.3580.4180.2230.910.750.910.820.750.82 -0.341-0.2020
3205.82 14.945.18 31.5912.82 28.730.890.790.830.921524.15560.0333.7116.0423.970.350.4280.2220.910.740.910.820.740.82 -0.341-0.20
4216.94 11.784.64 27.3315.93 31.490.890.790.830.922523.47555.3328.9719.8925.860.3620.4220.2160.910.740.910.810.740.81 -0.345-0.196-0
5226.08 14.54.88 33.4214.53 32.870.890.790.830.919574.25616.0135.8118.3327.160.3530.4310.2150.910.730.910.820.730.82 -0.344-0.195-0
6236.68 12.526.68 34.0918.4 28.130.90.790.830.926646.07673.1935.5122.6922.740.3520.4220.2260.90.740.90.820.740.82 -0.34-0.2040
7247.8 12.464.88 30.5418.9 36.80.890.790.830.92653.31695.1132.4523.8129.780.3640.4270.2090.910.730.910.810.730.81 -0.348-0.1910
8255.22 14.827.14 45.8917.35 28.960.90.790.830.923703.44738.948.1421.5923.480.340.4330.2270.90.740.90.830.740.83 -0.339-0.2030
9265.66 13.65.92 42.6318.64 33.430.890.790.830.921692.75733.0845.0623.4270.3480.4340.2180.910.730.910.820.730.82 -0.343-0.197-0
10277.36 12.264.88 35.6321.42 40.580.890.790.830.919722.84770.0237.9127.1332.450.3620.4320.2060.910.730.910.810.730.81 -0.349-0.1870
11288.22 144.1 35.1520.7 50.20.890.790.830.913784.64854.3838.2326.840.230.3680.4390.1930.910.710.910.80.710.8 -0.355-0.1760
12296.8 12.926.54 43.6323.56 36.510.890.790.830.922839.88881.0545.7329.5128.880.3530.4330.2140.910.730.910.820.730.82 -0.345-0.194-0
13307 136 43.7924.14 40.130.890.790.830.92861.17909.7346.2130.4731.690.3560.4350.2090.910.730.910.810.730.81 -0.347-0.19-0
14317.1 12.485 42.724.7 45.80.890.790.830.917840.37897.1945.5431.5236.110.360.4380.2010.910.720.910.810.720.81 -0.351-0.1830
15327.98 13.56.76 45.4527.29 41.930.890.790.830.921014.61066.6147.7334.432.740.3570.4350.2070.910.730.910.810.730.81 -0.348-0.1890
16336.7 11.444.66 45.1626.88 48.720.890.790.830.916837.17893.7648.1634.438.10.3610.440.1990.910.720.910.80.720.8 -0.352-0.1810
17345.32 12.746.58 59.2126.42 39.760.890.790.830.92913.15958.7662.0833.2931.070.3440.4420.2130.910.720.910.820.720.82 -0.345-0.192-0
18357.44 12.564.34 46.2327.83 57.230.890.790.830.913941.91018.9849.9536.1444.570.3650.4450.190.910.710.910.80.710.8 -0.356-0.1730
19366.68 12.64.72 51.528.24 54.380.890.790.830.914956.441029.1955.3536.542.290.3590.4470.1940.910.710.910.80.710.8 -0.354-0.1770
20375.6 114 53.9128.57 56.260.890.790.830.913841.25907.7958.1137.0643.690.3580.4490.1930.910.70.910.80.70.8 -0.354-0.1750
21385.12 11.446.14 64.8630.93 44.350.890.790.830.919958.31004.7667.9139.1134.140.3460.4450.2090.910.720.910.820.720.82 -0.347-0.189-0
22395.84 12.566.22 64.1731.65 48.230.890.790.830.9171072.191131.4367.6240.2937.060.3490.4470.2040.910.710.910.810.710.81 -0.349-0.1850
23405.6 14.746.16 71.0729.42 51.710.890.790.830.9141150.151230.1675.937.8940.030.3460.4540.20.910.710.910.810.710.81 -0.35-0.1810
24418.6 13.266.32 5636.93 56.990.890.790.830.9161331.51410.0459.2347.3543.160.3610.4450.1930.910.710.910.80.710.8 -0.354-0.1760
25428.84 11.224.12 49.0438.23 72.230.890.790.830.9111160.041253.9652.9550.0754.40.3730.4480.1790.920.690.920.790.690.79 -0.362-0.1630
26437.4 14.924.44 61.8632.26 73.410.890.790.830.9071265.011391.0567.9742.8156.170.3620.4590.1790.910.690.910.790.690.79 -0.36-0.1630
27446.02 12.747.4 75.1537.91 49.880.890.790.830.9181304.551361.8978.3448.0337.620.3460.4490.2040.910.710.910.810.710.81 -0.348-0.1850
28456.22 13.826.64 75.8736.66 56.270.890.790.830.9151352.231433.3880.3147.1142.60.3480.4540.1970.910.70.910.810.70.81 -0.351-0.1790
29467.36 13.27.52 71.6841.83 55.250.890.790.830.9171495.141563.9374.8853.2241.270.3520.4490.1980.910.710.910.810.710.81 -0.351-0.180
30476.04 13.444.48 73.6835.5 73.040.890.790.830.9081249.451364.6180.446.9455.390.3560.4620.1820.910.690.910.80.690.8 -0.358-0.1650
31486.16 13.266.88 81.3640.59 57.940.890.790.830.9151438.011516.485.6652.0543.40.3480.4550.1970.910.70.910.810.70.81 -0.351-0.1780
AVE:336.69 12.975.68 52.8933.66 35.680.890.790.830.918924.43982.9752.8933.6635.680.3550.440.2050.910.720.910.810.720.81-0.349-0.186-0




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