Econometric analysis of production function
Total Tillage explains almost all of the Production. A two-stage least squares method is used, where independent variables predict Total Tillage in the first stage, and the predicted value for Total Tillage is then used to explain Production in the second stage. These estimations confirm that Solow’s model (1957) is the most significant for explaining economic growth in Mato Grosso when considering TFP. More capital per worker, taken as land per worker, leads to higher production.
Table 6 - Multiple OLS Regression to Logarithmic Total Tillage with random effects
| lTotalTillage | |||
|---|---|---|---|
| lTFP | 0.370*** (8.43) |
Peixoto de Azevedo-Guarantã do Norte | -0.522 (-1.49) |
| lWAP | 0.267* (2.46) |
Juara | 0.841* (2.46) |
| lAnnualInvestment | 0.205** (3.22) |
Barra do Garças | 0.878* (2.34) |
| lCapitalStock | 0.182** (2.91) |
Confresa-Vila Rica | -0.238 (-0.65) |
| Tangará da Serra | 0.888* (2.54) |
Água Boa | 1.374** (2.72) |
| Diamantino | 0.622 (1.43) |
Rondonópolis | 0.676* (2.49) |
| Cáceres | -0.0716 (-0.28) |
Primavera do Leste | 1.441* (2.55) |
| Pontes e Lacerda-Comodoro | 0.932 (1.60) |
Jaciara | 0.578* (2.00) |
| Mirassol D'oeste | -1.186** (-2.62) |
year1980 | 0.594** (3.01) |
| Sinop | 0.422 (0.51) |
year1985 | 0.460* (2.22) |
| Sorriso | 1.750*** (5.37) |
year1995 | 0 (.) |
| Juína | -0.455 (-1.82) |
year2006 | 1.435*** (5.64) |
| Alta Floresta | -1.323 (-1.44) |
_cons | -1.329 (-1.25) |
| N = 126 | |||
t statistics in parentheses. * p < 0.05, ** p < 0.01, *** p < 0.001. Prob>chi2 = 0.0000. Source: BRASIL. IPEA, 2024a, BRASIL. IBGE, 2024b and 2024c . Elaborated by the author. Complete regression is located on ANNEX C.
Table 8 - Generalized 2SLS to Logarithm of Production with Random Effects
| (1) lProduction | |
|---|---|
| lTotalTillage | 0.850*** (25.65) |
| _cons | 2.487*** (7.38) |
| N = 126 | |
t statistics in parentheses. * p < 0.05, ** p < 0.01, *** p < 0.001. Prob>chi2 = 0.0000. Source: BRASIL. IPEA, 2024a, BRASIL. IBGE, 2024b and 2024c . Elaborated by the author. Complete regression is located on ANNEX C.
Table 9 - Coefficients of geographical production function in Table 7 multiplied by Table 8
| Region | Coefficient (Table 7) | Total increment (Table 7 x Table 8) |
|---|---|---|
| Tangará da Serra | 0.888* | 0.7548 |
| Diamantino | 0.622 | 0.5287 |
| Cáceres | -0.0716 | -0.06086 |
| Pontes e Lacerda e Comodoro | 0.932 | 0.7922 |
| Mirassol D'oeste | -1.186** | -1.0081 |
| Sinop | 0.422 | 0.3587 |
| Sorriso | 1.750*** | 1.4875 |
| Juína | -0.455 | -0.38675 |
| Alta Floresta | -1.323 | -1.12455 |
| Peixoto de Azevedo e Guarantã do Norte | -0.522 | -0.4437 |
| Juara | 0.841* | 0.71485 |
| Barra do Garças | 0.878* | 0.7463 |
| Confresa-Vila Rica | -0.238 | -0.2023 |
| Água Boa | 1.374** | 1.1679 |
| Rondonópolis | 0.676* | 0.5746 |
| Primavera do Leste | 1.441* | 1.22485 |
| Jaciara | 0.578* | 0.4913 |
t statistics in parentheses. * p < 0.05, ** p < 0.01, *** p < 0.001. Prob>chi2 = 0.0000. Source: BRASIL. IPEA, 2024a, BRASIL. IBGE, 2024b and 2024c . Elaborated by the author.
The Kandir Law, in this framework, promoted a structural break which allowed a boom of growth. Consequently, institutional changes paved the way for the Kandir Law to achieve this boom.
Table 12 - Chow test made for structural break in the Production Function of Mato Grosso
| Regressions | All years | until 1995 | only 2006 |
|---|---|---|---|
| n | 387 | 247 | 140 |
| SSR | 216.1206918 | 132.6288977 | 79.812138 |
| Chow statistics | 0.253787885 | ||
| P-value | 0.00018983920 | ||
| Economic conclusion | H0 rejected, Structural break reached, Kandir Law modified production function | ||
Source: BRASIL. IPEA, 2024a, BRASIL. IBGE, 2024b and 2024c . Elaborated by the author.
TFP during fiscal consolidation and after it is also different. Technology and other embodied factors in TFP are more present in the 21st century than until the 1980s. This also points out the constraint of Land Market leads to a more intensive production in technology, confirming the central thesis of a new trajectory for wealth generation in agriculture and cattle activities.
Table 17 - Chow test made for structural break in the coefficient of TFP
| Regressions | All years | until 1985 | only 2006 |
|---|---|---|---|
| n | 270 | 130 | 140 |
| SSR | 154.680624 | 69.334694 | 79.812138 |
| Chow statistics | 0.34320257 | ||
| P-value | 0.00182605962 | ||
| Economic conclusion | H0 rejected, Structural break reached, TFP is not equal during 1975-1985 and in 2006 | ||
Source: BRASIL. IPEA, 2024a, BRASIL. IBGE, 2024b and 2024c . Elaborated by the author.