Table 6-20 Regression results for sugar recovery, OLS and White-corrected estimates Variable
Coefficient
Prob(t>tc), OLS
Prob(t>tc), White-corrected
Small
-0.002
0.035
0.014
Year
0.009
0.115
0.156
Market price
0.000
0.888
0.901
Capacity
0.000
0.132
0.164
Pampanga
-0.252
0.041
0.000
Batangas
0.098
0.404
0.092
Cagayan
0.068
0.581
0.368
Camarines Sur
-0.359
0.004
0.000
Pangasinan
-0.216
0.167
0.017
Tarlac
0.027
0.830
0.704
Negros Occidental
-0.250
0.030
0.000
Negros Oriental
-0.052
0.654
0.378
Capiz
-0.333
0.007
0.000
Iloilo
-0.224
0.078
0.002
Cebu
-0.349
0.005
0.000
Leyte
-0.180
0.142
0.022
Bukidnon
0.049
0.693
0.450
Davao
0.158
0.215
0.054
Cotabato
-0.215
0.140
0.267
Source: Author’s calculations, based on SRA data.
c. Area allocation. Comparison of Tables 6-4 and 6-11 suggests a mixed correlation between sugarcane supply and the proportion of sugarcane area in the small farm category. The mill regions experiencing the biggest gain in proportion of small farms are Negros, Panay, and Mindanao. The first maintained its production share, the second suffered a declining production share, but the third experienced a rapid increase in production and area share. The study explores the relationship more systematically, again with multiple regression. The dependent variable is sugarcane area by province, as a percent of total farm area. An explanatory variable is the percent of provincial sugarcane area under small farms (denoted as Small_sh). The study adds other explanatory variables, namely time trend, output price, wage, fertilizer price, and provincial dummies. Prices are converted into real terms using the implicit agricultural GVA deflator, and lagged one period. This follows to some extent the supply response and acreage allocation literature (e.g. Rosegrant, Kasryno, and Perez, 1998; Khiem and Pingali, 1995; Bewley, Young, and Colman, 1987), but modified to take into account the study’s more modest aim of determining the role of small-scale farming decreasing the share of sugarcane vis-à-vis all other crops. Results are shown in Table 6-21. The adjusted R-squared for this model is 0.952. The coefficient of Small_sh is statistically significant. Surprisingly, the effect is positive. The
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