# VIII. Econometric Results

## 8.1 Mean Hypotheses Testing

In this section several hypotheses regarding the means of the variables considered in the econometric model are tested. These tests are quite simple and intended to characterize the population studied. Some examples of the tested hypotheses are: do larger farmers earn larger profits? Do larger farmers receive higher output prices? and so on. Questions related to efficiency will more properly de dealt with within the context of the frontier profit function; nonetheless a better characterization of the population on matters related to efficiency may be helpful and recommended. The tests of the population means were carried out by adjusting linear regression of each variable of interest on a set of dummy variables standing for farm size, farmer's education and other characteristics of the farm.

Next the test results are spelled, a figure with the variable means is presented and the statistical estimates reported for each hypothesis. When necessary some explanation is added.

8.1.1 Broiler

1) Unit profit is higher for large-scale producers.

Source: CEPEA/ESALQ/USP

 Variable Coeff T-Stat Signif Constant 0.049 10.013 0.000 R 0.014 2.663 0.008

R = 1 if large-scale producers, R = 0 if small-scale producers

Small-scale producers have lower profits per unit of output than do large-scale producers. This direct relationship between scale and profit may be related to technology and other possible market advantages possessed by larger producers.

2) Feed conversion ratios are higher for small-scale producers.

Source: CEPEA/ESALQ/USP

 Variable Coeff T-Stat Signif Constant 1.941 81.562 0.000 R -0.058 -2.249 0.026

R = 1 if large-scale producers, R = 0 if small-scale producers

This is an expected result, since larger farms would use more intensive technology and have better conditions to reduce input losses in the production process.

3) Large producers receive higher prices for output than small producers.

Source: CEPEA/ESALQ/USP

 Variable Coeff T-Stat Signif Constant 0.070 21.849 0.000 R 0.011 3.161 0.002

R = 1 if large producers, R = 0 if small producers

Larger farmers receive higher prices; possible reasons are lower transportation costs and more homogeneous product in the case of larger producers.

4) Large producers pay higher prices of input.

Source: CEPEA/ESALQ/USP

 Variable Coeff T-Stat Signif Constant 0.156 37.003 0.000 R 0.010 2.129 0.034

R = 1 if large producers, R = 0 if small producers

Source: CEPEA/ESALQ/USP

 Variable Coeff T-Stat Signif Constant 0.014 18.649 0.000 R 0.002 2.849 0.005

R = 1 if large producers, R = 0 if small producers

Price of Litter

Source: CEPEA/ESALQ/USP

 Variable Coeff T-Stat Signif Constant 10.225 2.528 0.013 R 14.580 3.326 0.001

R = 1 if large producers, R = 0 if small producers

This is an unexpected result. One possible explanation would be the fact that small-scale farmers are mainly located in Santa Catarina where labor and electricity would be cheaper. In the Center West, where larger farmers are, electricity is either imported from other regions or produced through pore expensive thermoelectric process.

5) Large producers are better educated than the small ones.

Source: CEPEA/ESALQ/USP

 Variable Coeff T-Stat Signif Constant 2.353 7.750 0.000 R 0.909 2.762 0.006

R = 1 if large producers, R = 0 if small producers

6) Higher education does not lead to higher profits.

Source: CEPEA/ESALQ/USP

 Variable Coeff T-Stat Signif Constant 0.062 27.595 0.000 E1 0.002 0.437 0.663 E2 -0.013 -1.715 0.088

E1 = 0 if E<=complete primary; E1 = 1 if otherwise
E2 = 0 if < complete college; E2 = 1 if otherwise

In general education was not related to profit. However, it was observed that college education is negatively related to profit. A possible reason for this latter result would be a lower dedication of better educated farmers to the farm activity.

7) Small producers have higher expenditures per unit of output on environmental mitigation costs.

Source: CEPEA/ESALQ/USP

 Variable Coeff T-Stat Signif Constant 0.008 6.494 0.000 R -0.002 -1.658 0.099

R = 1 if large producers, R = 0 if small producers

The result shows that small producers have higher expenditures per unit of output on environmental mitigation costs possibly due to higher manure spreading costs.

7) Producers with higher density of animals have lower expenditures per unit of output on environmental mitigation costs.

Source: CEPEA/ESALQ/USP

 Variable Coeff T-Stat Signif Constant 0.008 10.792 0.000 D1 -0.002 -2.337 0.020 D2 -0.003 -2.080 0.039

D1 = 0 if D<=500 broilers/ha; D1 = 1 if otherwise
D2 = 0 if <=3000broilers; D2 = 1 if otherwise

The result shows that the producers with higher density of animals have lower expenditure per unit of output on environmental mitigation costs. One possible explanation may be the fact that farms with higher animal density sell a larger proportion of the manure while the others face the costs of storing and spreading it over the farm.

8) Producers located near to residential centers or to centers of economic activity do not have higher expenditures per unit of output on environmental mitigation costs.

Source: CEPEA/ESALQ/USP

 Variable Coeff T-Stat Signif Constant 0.006 11.252 0.000 L1 0.000 0.039 0.969 L2 0.002 0.791 0.430

L1 = 0 if L<=20 km; L1 = 1 otherwise
L2 = 0 if <=40 km; L2 = 1 if otherwise

9) Producers located in the central west region have lower expenditures per unit of output on environmental mitigation costs.

Source: CEPEA/ESALQ/USP

 Variable Coeff T-Stat Signif Constant 0.007 10.419 0.000 C -0.002 -1.708 0.089

C = 0 if South Region; C = 1 if West Central

This was an expected result because farmers in the Center West tend to sell the manure with the exception of those who willingly produce manure for fertilizing reasons.

10) The importance of the sales of manure and other non meat products is not higher for the small producers' profit.

Source: CEPEA/ESALQ/USP

 Variable Coeff T-Stat Signif Constant 0.109 4.988 0.000 R 0.037 1.583 0.115

R = 1 if large producers; R = 0 if small producers

11) Swine farmers are better educated than the poultry and milk ones

Source: CEPEA/ESALQ/USP

 t-ratio Dairy X Broiler 2.678 *** Dairy X Swine - 4.309 *** Broiler X Swine -6.368 ***

*** Significant 1%.

8.1.2 Layer

1) Unit profit is not higher for large-scale producers.

Source: CEPEA/ESALQ/USP

 Variable Coeff. T-Stat Signif. Constant -1.317 -0.687 0.494 R 0.988 0.498 0.620

R = 0 if small-scale producers; R = 1 if large-scale producers

2) Feed conversion ratios are not higher for large-scale farmers.

Source: CEPEA/ESALQ/USP

 Variable Coeff T-Stat Signif Constant 50.162 17.821 0.000 R 2.052 0.704 0.483

R = 1 if large-scale producers, R = 0 if small-scale producers

3) Large producers do not pay lower prices of input.

Source: CEPEA/ESALQ/USP

 Variable Coeff T-Stat Signif Constant 0.474 18.173 0.000 R -0.019 -0.697 0.487

R = 1 if large producers, R = 0 if small producers

Price electricity

Source: CEPEA/ESALQ/USP

 Variable Coeff T-Stat Signif Constant 0.155 20.347 0.000 R 0.014 1.764 0.081

R = 1 if large producers, R = 0 if small producers

In the case of feed, no difference was observed, but for electricity, larger farmers pay higher prices.

4) Large producers are not better educated than the small ones.

Source: CEPEA/ESALQ/USP

 Variable Coeff T-Stat Signif Constant 4.000 7.197 0.000 R 0.904 1.570 0.120

R = 1 if large producers, R = 0 if small producers

5) Higher education leads to higher profits.

Source: CEPEA/ESALQ/USP

 Variable Coeff T-Stat Signif Constant -0.434 -0.431 0.667 E1 -0.275 -0.201 0.841 E2 0.534 0.452 0.653

E1 = 0 if E <= complete primary; E1 = 1 if otherwise
E2 = 0 if < high school (secondary) complete; E2 = 1 if otherwise

The education level was not related to the level of profit.

6) Small producers, other things equal, have higher expenditures per unit of output on environmental mitigation costs.

Source: CEPEA/ESALQ/USP

 Variable Coeff T-Stat Signif Constant 0.104 4.098 0.000 R -0.061 -2.306 0.023

R = 1 if large producers, R = 0 if small producers

The result showing that small producers have higher expenditures per unit of output on environmental mitigation costs, possibly due to higher manure spreading costs.

6) Producers with higher density of animals have lower expenditures per unit of output on environmental mitigation costs.

Source: CEPEA/ESALQ/USP

 Variable Coeff T-Stat Signif Constant 0.069 5.783 0.000 D1 -0.035 -2.142 0.035 D2 0.006 0.398 0.691

D1 = 0 if D<=0,1 chicken/m2; D1 = 1 if otherwise
D2 = 0 if <= 0,4 chicken/m2; D2 = 1 if otherwise

The result shows that the producers with more than 0.1 chicken/ m2 have lower expenditure per unit of output on environmental mitigation costs..

7) Producers located near to residential centers or to centers of economic activity have high expenditures per unit of output on environmental mitigation costs.

Source: CEPEA/ESALQ/USP

 Variable Coeff T-Stat Signif Constant 0.033 3.938 0.002 L1 0.047 2.845 0.006 L2 -0.021 -0.985 0.327

L1 = 0 if <=5 km; L1 = 1 if otherwise
L2 = 0 if <=10 km; L2 = 1 if otherwise

The result shows that the producers located near (less than 5 km) to residential centers or to centers of economic activity have lower expenditures per unit of output on environmental mitigation costs.

8) There is no difference in terms of the importance of the sales of manure and other non meat/dairy products between small and large producers' profit.

Source: CEPEA/ESALQ/USP

 Variable Coeff T-Stat Signif Constant 0.047 3.301 0.001 R -0.002 -0.120 0.904

R = 1 if large producers; R = 0 if small producers

8.1.3 Swine

1) Unit profit is not higher for large-scale producers.

Source: CEPEA/ESALQ/USP

 Variable Coeff Teste-t Signif Constant -0.251 -2.969 0.004 D 0.103 1.126 0.262

D = 0 if small-scale producers; D =1 if large-scale producers

Both small and large producers faced losses instead of profits. Statistically no difference was detected between theses losses.

2) Feed conversion ratios are not higher for large-scale producers.

Source: CEPEA/ESALQ/USP

 Variable Coeff Teste-t Signif Constant 2.221 27.727 0.000 D 0.110 1.261 0.209

D = 0 if small-scale producers, D = 1 if large-scale producers

3) Unit profits are higher for integrated/cooperative producers than for independent producers.

Source: CEPEA/ESALQ/USP

 Variable Coeff Teste-t Signif Constant -0.395 -7.381 0.000 D1 0.373 5.682 0.000 D2 -0.039 -0.612 0.542

D1 = 0 if independent, D1 = 1 if otherwise
D2 = 1 for integrate; D2 = 0 if otherwise

Independent farmers had lower profits than integrated farmers or cooperatives. The profit of the latter two are not different however.

3) The conversion ratios are not higher for integrated producers.

Source: CEPEA/ESALQ/USP

 Variable Coeff Teste-t Signif Constant 2.498 46.910 0.000 D1 -0.246 -3.759 0.000 D2 -0.059 -0.924 0.357

D1 = 0 if independent; D1 = 1 if otherwise
D2 = 1 if integrate; D2 = 0 if otherwise

The conversion rate is higher for independent farmers, but not different between integrated and cooperative farmers. A possible explanation may be the rigidity of the integrated system in terms the feed supply to animals.

4) Large producers do not receive higher prices for output than small producers.

Source: CEPEA/ESALQ/USP

 variable Coeff Teste-t Signif Constant 130.055 11.840 0.000 D -7.313 -0.612 0.542

D = 0 if small producers, D = 1 if large producers

6) Large producers do not pay lower prices of input.

Source: CEPEA/ESALQ/USP

 Variable Coeff Teste-t Signif Constant 0.447 22.030 0.000 D 0.020 0.906 0.366

D = 0 if for small producers, D = 1if large producers

There is no statistically significant difference in terms of input prices between larger and small farmers.

7) Larger producers do not receive higher output prices in integrated system.

Source: CEPEA/ESALQ/USP

 Variable Coeff Teste-t Signif Constant 135.432 44.207 0.000 D -2.228 -0.607 0.547

D = 0 if small producers; D = 1 if large producers

Within the integrated system there is no significant difference in terms of output prices between large and small farmers.

8) xLarger producers do not pay lower input prices within the integrated systems.

Source: CEPEA/ESALQ/USP

 Variable Coeff Teste-t Signif Constant 0.445 17.535 0.000 D -0.019 -0.611 0.545

D = 0 if small producers; D = 1 if large producers

Within the integrated system large and small farmers pay statistically input prices equal.

9) Large are producers better educated than the small ones.

Source: CEPEA/ESALQ/USP

 Variable Coeff Teste-t Signif Constant 2.500 6.603 0.000 D 2.273 5.515 0.000

D = 0 if small producers; D = 1 if large producers

10) Higher education does not lead to higher profits.

Source: CEPEA/ESALQ/USP

 Variable Coeff Teste-t Signif Constant -0.094 -1.440 0.152 D1 -0.055 -0.507 0.614 D2 -0.049 -0.507 0.613

D1 = 0 if < 5 years of education; and D1 = 1 if otherwise
D2 = 0 if < 10 years of education and D2 = 1 if otherwise

Unit profit is not affected by the level of education of the farmer.

11) Independent farmers are better educated than the integrated ones.

Source: CEPEA/ESALQ/USP

 Variable Coeff Teste-t Signif Constant 8.603 15.554 0.000 D 2.689 3.196 0.002

D = 0 if integrate; D = 1 if independent

Independent farmers presented higher level of education.

11) Small producers, other things equal, does not have higher expenditures per unit of output on environmental mitigation costs.

Source: CEPEA/ESALQ/USP

 Variable Coeff Teste-t Signif Constant 0.034 6.722 0.000 D -0.006 -1.064 0.289

D = 0 if small producers; D = 1 if large producers

12) Producers with higher density of animals have higher expenditures per unit of output on environmental mitigation costs.

Source: CEPEA/ESALQ/USP

 Variable Coeff Teste-t Signif Constant 0.000 0.094 0.925 D1 0.002 0.278 0.781 D2 0.023 3.564 0.001

D1 = 0 if <= 0,00094 animal/m2; D1 = 1 if otherwise
D2 = 1 if > 0,00366 animal/m2; D2 = 0 if otherwise

Producers with than 0,00366 animal/m2 have higher expenditures per unit of output on environmental mitigation costs. This may due to higher spreading costs associated to longer distances or larger areas to be covered.

13) Farms located near residential centers or to centers of economic activity does not have higher expenditures per unit of output on environmental mitigation costs.

Source: CEPEA/ESALQ/USP

 Variable Coeff Teste-t Signif Constant 0.031 11.210 0.000 D -0.005 -1.243 0.216

D = 0 if <= 9 km; D = 1 if > 9 km

14) Farms located in the central west region do not have lower expenditures per unit of output on environmental mitigation costs.

Source: CEPEA/ESALQ/USP

 Variable Coeff Teste-t Signif Constant 0.027 11.046 0.000 D 0.005 1.214 0.227

D = 1 if Central West; D = 0 if otherwise

No significant difference was observed in terms of expenditures per unit of output on environmental mitigation costs between farmers from the Center West and from other regions.

15) Quality and sanity regulations are not costlier to small producers.

Source: CEPEA/ESALQ/USP

 Variable Coeff Teste-t Signif Constant 0.096 2.987 0.003 D -0.047 -1.347 0.180

D = 0 if small producers; D = 1 if large producers

8.1.4 Dairy

1) Unit profit is not higher for large-scale producers

Source: CEPEA/ESALQ/USP

 Variable Coeff. Test-t Signific Constant 0.037 2.160 0.032 R1 -0.008 -0.299 0.766 R2 0.022 0.986 0.325

R1= 0 if <50 heads; R1 = 1 if otherwise
R2 = 1 if >70 heads; R2 = 0 if otherwise

2) Milk production per cow is not higher for large scale producers.

Source: CEPEA/ESALQ/USP

 Variable Coeff. Test-t Signific Constant 19.545 15.257 0,000 R1 -0.797 -0.391 0.697 R2 -0.828 -0.496 0.621

R1= 0 if <50 heads; R1 = 1 if otherwise
R2 = 1 if >70 heads; R2 = 0 if otherwise

3) Large producers receive higher output price than small producers.

Source: CEPEA/ESALQ/USP

 Variable Coeff. Test-t Signific Constant 0.331 23.488 0.000 R1 0.007 0.321 0.749 R2 0.039 2.107 0.037

R1= 0 if <50 heads; R1 = 1 if otherwise
R2 = 1 if >70 heads; R2 = 0 if otherwise

Dairy farmers with more than 70 cows do receive higher milk prices than smaller farmers. Possible reasons are lower transportation cost due to larger volumes and production of better quality milk.

4) Large producers do not pay higher prices of input

Source: CEPEA/ESALQ/USP

 Variable Coeff. Test-t Signific Constant 712.58 9.982 0.000 R1 102.16 0.898 0.370 R2 -65.68 -0.707 0.481

R1= 0 if <50 heads; R1 = 1 if otherwise
R2 = 1 if >70 heads; R2 = 0 if otherwise

5) Large producers better educated than the small ones

Source: CEPEA/ESALQ/USP

 Variable Coeff. Test-t Signific Constant 7.400 7.784 0.000 R1 2.908 1.920 0.057 R2 -1.048 -0.846 0.398

R1= 0 if <50 heads; R1 = 1 if otherwise
R2 = 1 if >70 heads; R2 = 0 if otherwise

Larger farmers (50 heads or more) are better educated than smaller farmers.

6) There is no significant relationship between education and unit profit of milk farmers.

Source: CEPEA/ESALQ/USP

 Variable Coeff. Test-t Signific Constant 0.055 5.443 0.000 E1 -0,016 -1.018 0.310 E2 -0,004 -0.282 0.778

E1 = 0 if <5 years; E1 = 1 if otherwise
E2 = 1 if > 10 years; E2 = 0 if otherwise

7) Small farmers do not have higher expenditures per unit of on environmental mitigation cost.

Source: CEPEA/ESALQ/USP

 Variable Coeff. Test-t Signific Constant 0.005 1.866 0.0639 R1 0.004 0.991 0.3231 R2 -0.001 -0.321 0.7485

R1= 0 if <50 heads; R1 = 1 if otherwise
R2 = 1 if >70 heads; R2 = 0 if otherwise

8) Producers with higher density of animals have lower expenditures per unit of output on environmental mitigation cost.

Source: CEPEA/ESALQ/USP

 Variable Coeff. Test-t Signific Constant 0.017 13.900 0.000 D1 -0.013 -7.039 0.000 D2 -0.004 -1.820 0.071

D1= 0 if <0.5 heads/ha; D1 = 1 if otherwise
D2 = 1 if >1.0 heads/ha; D2 = 0 if otherwise

9) Farms located near residential centers or to centers of economic activity does not have higher expenditures per unit of output on environmental mitigation costs.

Source: CEPEA/ESALQ/USP

 Variable Coeff. Test-t Signific Constant 0.0074 5.258 0.000 L1 0.0011 0.533 0.595 L2 -0.0013 -0.446 0.656

L1= 0 if <20 km; L1 = 1 if otherwise
L2 = 1 if >50 km; D2 = 0 if otherwise

10) Producers located in the center west region have higher expenditures per unit of output on environmental mitigation cost

Source: CEPEA/ESALQ/USP

 Variable Coeff. Test-t Signific Constant 0.005 3.328 0.001 C 0.006 3.287 0.001

C = 1 if (SP+GO+MG); C = 0 if otherwise

11) Farmers spending more on mitigation costs have lower unit profits.

Source: CEPEA/ESALQ/USP

 Variable Coeff. Test-t Signific Contant 0.058 7.594 0.000 M1 -0.014 -1.032 0.304 M2 -0.053 -3.021 0.003

M1 = 1 if < 0.006 R\$/litre; M1 = 0 if otherwise
M2 = 1 if < 0.016 R\$/litre; M2 = 0 if otherwise

## 8.2 Results of stochastic profit frontier

8.2.1. Econometric procedures

The stochastic frontier model, which is considered in this study, is defined by:

(1)

where:

Yi denotes the output for the i-th firm;

xi represents a (1 X K) vector whose values are functions of input and output prices and other explanatory variables for the i-th firm (fixed factors and prices);

b is a (K X 1) vector of unknown parameters to be estimated;

the vi are assumed to be independent and identically distributed random errors which have normal distribution with zero mean and unknown variance, , and

the ui are non-negative unobservable random variables associated which the inefficiency of production.

In the model, the technical inefficiency effects are defined by

(2)

where:

zi is a (1 X M) vector of explanatory variables associated which the technical inefficiency effects;

d is an (M X 1) vector of unknown parameters to be estimated; and

the wi are unobservable random variables, which are assumed to be independent and identically distributed non-negative truncations of normal distributions with mean zero and variance constant (s2).

The method of maximum likelihood is proposed for simultaneous estimation of the parameters of the stochastic frontier and the model for the technical inefficiency effects. The program FRONTIER 4.1 written by Tim Coelli (as described in Coelli 1996 - model 2 or "technical efficiency effects model") is used to obtain estimates for parameters.

There is particular interest in testing the null hypothesis that the technical inefficiency effects are not present in the model. The null hypothesis that the technical inefficiency effects are not random is expressed by where:

If g is too close to one, inefficiency effects are important explainers of profit across farms and random noise is not important. Further, the null hypothesis that the inefficiency effects are not influenced by the level of the explanatory variables is expressed by where d' denotes the vector, d, with a constant term omitted, given that it is to included in the expression, zitd.

A Cobb-Douglas production frontier using cross-sectional data was estimated. In all cases, broiler, dairy; swine and layer, the dependent variable is expressed in natural logarithm. Explanatory variables of the stochastic frontier models (variable costs and fixed factors) and explanatory variables of the technical inefficiency models are also in natural logarithm form.

8.2.2. Results for broiler

The maximum-likelihood estimates of the parameters in the stochastic frontier profit function, given the specifications for the technical inefficiency effects, defined by equations (1) and (2) are given in Table 8.1.

Table 8.1 Results for the stochastic frontier profit function - Broiler.

 Variable Coefficients Standard-error t-ratio b0 Constant -1.259 0.416 -3.026*** b1 Feed Conversion 0.640 0.202 3.171*** b2 Price Hired Labor -0.032 0.010 -3.083*** b3 Price of Electricity n.s. - - b4 Price of Litter n.s. - - b5 Collecting and Truck Loading Price n.s. - - b6 Heating Price n.s. - - b7 Output Broiler Price 0.752 0.063 11.951*** b8 Agricultural Land n.s. - - b9 Family Labor -0.215 0.092 -2.346** b10 Capital n.s. - - d0 Constant -2.626 1.285 -2.043** d1 Length of Time Decision Maker in Activity -0.145 0.079 -1.832* d2 E1 Dummy (0 for < 2,1 for others) n.s. - - d3 E2 Dummy (0 for < = 6,1 for others) 0.588 0.303 1.939* d4 Length of Time Decision Maker in this Farm n.s. - - d5 Animal Concentration in Regions -0.372 0.219 -1.694* d6 Animal Concentration on Farm n.s. - - d7 Environmental Cost 1.777 0.713 2.491** d8 Distance of the City n.s. - - d9 Taxes -2.551 0.990 -2.577*** d10 Information Index 2.118 0.163 13.018*** d11 DummyPR -0.792 0.330 -2.400** d12 DummyRS -2.376 0.963 -2.466** d13 DummyMG 1.427 0.436 3.274*** d14 DummyMS -1.330 0.602 -2.210** d15 DummyMT -1.910 0.721 -2.650*** d16 DummyGO -3.322 0.850 -3.907*** s2 Sigma-squared 0.473 0.051 9.202*** g Gamma 0.965 0.008 123.806***

Source: CEPEA/ESALQ/USP
* Significant 10%
** Significant 5%
*** Significant 1%
Note: All state dummy variables are zero for Santa Catarina.

Mean Efficiency by Stratum

Source: CEPEA/ESALQ/USP

Mean Efficiency by Region

Source: CEPEA/ESALQ/USP

Mean Efficiency by Scale

Source: CEPEA/ESALQ/USP

Mean Efficiency by State

Source: CEPEA/ESALQ/USP

Based on the LR test, the stochastic frontier estimation is statistically different from the OLS estimation in which the technical effects are assumed to be absent (i. e., Ui = 0 for all farmers). The generalized likelihood-ratio statistic for testing for the absence of the technical inefficiency effects from the frontier is calculated to be 512.447. Hence the null hypotheses of no technical inefficiency effects are rejected (critical values in Kodde and Palm (1986)). The estimated of gamma of 0.965 is also clearly different from zero (standard-error = 0,008), suggesting that the auxiliary equation (the technical efficiency equation) play an important role in the estimation of the frontier function. The gamma-estimate is not significantly different from one, which indicates that the stochastic frontier model may not be significantly different from the deterministic frontier, in which there are no random errors in the profit function.

The numbers of observations used in the fitting of the broiler model was 229. Mean efficiency for broiler is 0,860.

8.2.3. Results for layer

Table 8.2 shows the maximum-likelihood estimates of the parameters in the stochastic frontier profit function, given the specifications for the technical inefficiency effects.

Table 8.2 Results for the stochastic frontier profit function - layer

 Variable Coefficients Standard-error t-ratio b0 Constant -101.202 1.773 -57.091*** b1 Feed Conversion -1.443 0.216 -6.680*** b2 Feed Price -1.257 0.201 -6.259*** b3 Price of Hired Labor -0.169 0.085 -1.997** b4 Price of Electricity n.s. - - b5 Price Freight n.s. - - b6 Agricultural Land 39.232 0.695 56.415*** b7 Family Labor n.s. - - b8 Capital -0.060 0.034 -1.772* d0 Constant -16.126 2.202 -7.323*** d1 Length of Time Decision Maker in this Farm n.s. - - d2 Level of Education for Decision Maker 3.253 0.511 6.371*** d3 Age of the Decision Maker n.s. - - d4 Length of Time Decision Maker in Activity n.s. - - d5 Animal Concentration in Regions 1.843 0.309 5.962*** d6 Animal Concentration on the Farm n.s. - - d7 Environmental Cost 1.345 0.237 5.679*** d8 Distance of the City 1.136 0.090 12.555*** d9 Information Index n.s. - - d10 DummySP -10.336 0.642 -16.088*** d11 DummyPR -4.527 0.568 -7.972*** s2 Sigma-squared 2.743 0.158 17.392*** g Gamma 0.998 0.001 1312.501***

Source: CEPEA/ESALQ/USP
* Significant 10%
** Significant 5%
*** Significant 1%
Note: All state dummy variables are zero for Minas Gerais.

The means efficiency by stratum, by State, by scale and by region is presented in the pictures below.

Mean Efficiency by Stratum

Source: CEPEA/ESALQ/USP

Mean Efficiency by Scale

Source: CEPEA/ESALQ/USP

Mean Efficiency by State

Source: CEPEA/ESALQ/USP

The gamma-estimate for layer is not significantly different from one, which indicates that the stochastic frontier model may not be significantly different from the deterministic frontier. The estimate for Gamma is 0.998 (too close to 1) and the estimate for standard error is low (0.001). The LR test is also significant (268.602) showing that the null hypothesis of no technical inefficiency effects is rejected.

The numbers of observations used in the layer model was 89. The mean efficiency is 0.817.

8.2.4. Results for swine

The maximum-likelihood estimates of the parameters in the stochastic frontier profit function, given the specifications for the technical inefficiency effects are given in Table 8.3.

Table 8.3 Results for the stochastic frontier profit function - swine.

 Variable Coefficients Standard-error t-ratio b0 Constant n.s. - - b1 Feed Conversion -0.816 0.150 -5.451*** b2 Price of Feed -0.677 0.103 -6.588*** b3 Price of Hired Labor n.s - - b4 Price of Electricity n.s - - b5 Price of Environmental n.s - - b6 Price of Output 0.534 0.178 2.991*** b7 Dummy Complete Cycle (1=yes or 0=no) -0.218 0.054 -4.074*** b8 Dummy Independent (1=yes or 0=no) n.s - - b9 Dummy Integrate (1=yes or 0=no) 0.102 0.057 1.774* b10 Agriculture Land 43.883 1.666 26.333*** b11 Family Labor n.s - - b12 Capital n.s - - d0 Constant n.s - - d1 Length of Time Decision Maker in this Farm 0.203 0.121 1.676* d2 Level of Education for Decision Maker -0.567 0.242 -2.348** d3 Age of the Decision Maker d4 Length of Time Decision Maker in Activity -0.256 0.148 -1.726* d5 Animal Concentration in Region -0.388 0.153 -2.540** d6 Animal Concentration on the Farm -6.649 3.743 -1.776* d7 Environmental Cost 8.098 4.650 1.742* d8 Distance of the City -0.325 0.109 -2.982*** d9 Farm Distance to Nearest Neighbor 0.206 0.095 2.182** d10 Information Index 0.643 0.361 1.782* d11 Dummy SC -1.205 0.356 -3.383*** d12 Dummy PR n.s - - d13 Dummy MS -2.816 0.707 -3.983*** d14 Dummy MT -4.911 1.509 -3.254*** d15 Dummy GO -2.601 0.923 -2.817*** d16 Dummy RS n.s - - d17 Percent Share of Swine Production Total Income n.s - - s2 Sigma-squared 0.478 0.123 3.892*** g Gamma 0.986 0.007 148.389***

Source: CEPEA/ESALQ/USP
* Significant 10%
** Significant 5%
*** Significant 1%
Note: All state dummy variables are zero for Minas Gerais.

The means efficiency by stratum, by State, by scale and by region is presented in the pictures below.

Mean Efficiency by Stratum

Source: CEPEA/

Mean Efficiency by Scale

Source: CEPEA/ESALQ/USP

Mean Efficiency by State

Source: CEPEA/ESALQ/USP

Mean Efficiency by Regions

Source: CEPEA/ESALQ/USP

The estimated of gamma of 0,986 (standard errors = 0,007) is significant showing that technical efficiency equation is important in the estimation of the frontier function. The LR test is significant (100.270) suggesting that the null hypothesis related with no technical inefficiency effects is rejected.

The numbers of observations used in the fitting of the swine model was 141. Mean efficiency for swine is 0,767.

8.2.4. Results for dairy

Table 8.4 showing the maximum-likelihood estimates of the parameters in the stochastic frontier profit function, given the specifications for the technical inefficiency effects, defined by equations (1) and (2).

Table 8.4 Results for the stochastic frontier profit function - dairy.

 Variable Coefficients Standard-error t-ratio b0 Constant -1.682 0.367 -4.588*** b1 Milk Prod. by Cow in Lactation per Day 0.268 0.071 3.777*** b2 Humid Feed Price -0.071 0.017 -4.238*** b3 Dry Feed Price -0.194 0.036 -5.345*** b4 Medicine Price -0.063 0.024 -2.658*** b5 Genetic Price 0.025 0.011 2.372** b6 Electricity Price n.s - - b7 Hired Labor Price -0.048 0.019 -2.547** b8 Output Price 0.846 0.093 9.121*** b9 Membership of a Cooperative n.s - - b10 Agricultural Land n.s - - b11 Family Labor -0.122 0.041 -2.973*** b12 Capital n.s - - b13 Value of Herd -3.541 1.110 -3.190*** d0 Constant -3.691 1.504 -2.454** d1 Duration of Lactation in the Farm -4.120 0.581 -7.092*** d2 Experience in the Activity 1.311 0.221 5.929*** d3 Dummy Propri -1.925 0.821 -2.345** d4 Dummy Family -3.397 0.924 -3.676*** d5 Manager Experience in the Activity n.s - - d6 DummyMan -1.703 0.603 -2.824*** d7 Age of Manager 4.614 0.833 5.541*** d8 Manager Schooling 2.005 0.244 8.218*** d9 DummyTrain 1.369 0.484 2.828*** d10 Distance between the Farm and the City n.s - - d11 Information Index -0.502 0.161 -3.123*** d12 Environmental Cost n.s - - d13 DummyRS n.s - - d14 DummySC 1.904 0.790 2.410** d15 DummyPR n.s - - d16 DummySP -2.748 0.641 -4.288*** d17 DummyMG n.s - - s2 Sigma-squared 1.558 0.213 7.310*** g Gamma 0.998 0.001 716.032***

Source: CEPEA/ESALQ/USP
* Significant 10%
** Significant 5%
*** Significant 1%
Note: All state dummy variables are zero for Goias.

The mean efficiency by stratum, by State, by scale and by region is presented in the pictures below.

Mean Efficiency by Stratum

Source: CEPEA/ESALQ/USP

Mean Efficiency by Scale

Source: CEPEA/ESALQ/USP

Mean Efficiency by State

Source: CEPEA/ESALQ/USP

Mean Efficiency by Regions

Source: CEPEA/ESALQ/USP

The numbers of observations used in the fitting of the dairy model was 160. Mean efficiency for dairy is 0,756.

The gamma estimated is 0,998 (standard errors = 0,001) clearly significant, showing that the model is no equivalent to the average response function, which can be efficiently estimated by ordinary least-square (OLS). The LR test = 291.304 is also significant showing that the null hypotheses of no technical inefficiency effects is rejected.

Using the same methodology for the profit, the equation with dummies variables were adjusted to verify if the efficiency is affected by scale and region where the farm is located. The results are showing below:

Broiler

 Variable Coeff T-Stat Signif Constant 0.880 73.470 0.000 C1 -0.040 -2.365 0.019

C1 = 0 if South Region; C1 = 1 if otherwise

 Variable Coeff T-Stat Signif Constant 0.849 37.780 0.000 R1 0.013 0.531 0.596

R1 = 1 if large-scale producers, R1 = 0 if small-scale producers

Layer

 Variable Coeff T-Stat Signif Constant 0.747 12.130 0.000 R1 0.075 1.171 0.245

R1 = 1 if large-scale producers, R1 = 0 if small-scale producers

Swine

 Variable Coeff T-Stat Signif Constant 0.722 29.587 0.000 C1 0.084 2.531 0.012

C1 = 0 if South Region; C1 = 1 if otherwise

 Variable Coeff T-Stat Signif Constant 0.714 16.378 0.000 R1 0.062 1.306 0.194

R1 = 1 if large-scale producers, R1 = 0 if small-scale producers

Dairy

 Variable Coeff T-Stat Signif Constant 0.761 32.609 0.000 C1 -0.008 -0.263 0.793

C1 = 0 if South Region; C1 = 1 if otherwise

 Variable Coeff. Test-t Signific Constant 0.774 19.754 0.000 R1 -0.039 -0.665 0.507 R2 0.022 0.450 0.653

R1= 0 if <50 heads; R1 = 1 if otherwise
R2 = 1 if >70 heads; R2 = 0 if otherwise

In the broiler case, the results show that the efficiency does not depend on scale, but is higher in the South region if compared to the other. Also, statistics differences between small and large producers were not observed for layer.

For the swine, the efficiency does not depend on scale, but depends on the region where the farm is located. The producers whose farms are localized in other regions (except the South) have higher efficiency.

In the dairy case, the efficiency does not show statistics differences, neither relatively to scale or region where farms are located.