Table 5 Subsidy simulations
From: Predicting minimum tillage adoption among smallholder farmers using micro-level and policy variables
Predicted probability of CA adoption by sample | |||||
|---|---|---|---|---|---|
SER level | Whole sample | Ethiopia | Kenya | Malawi | Tanzania |
Base Level (A) | 0.168*** (0.004) | 0.258*** (0.008) | 0.039*** (0.004) | 0.338*** (0.009) | 0.099*** (0.008) |
Panel I: effect of changing subsidy expenditure as a percentage of agricultural budgets spent on farm input subsidies (SER). Set SER at Malawi’s level | |||||
 At Malawian mean (C) | 0.319*** (0.67) | 0.572*** (0.126) | 0.140*** (0.057) | NA | 0.143** (0.019) |
  Chi-square tests A = B | 5.12** | 6.38** | 3.11* | NA | 5.62** |
  Elasticities of adoption with respect to SER A to B | 1.194 | 0.261 | 1.233 | NA | 1.585 |
Panel II: effect of low subsidy with full credit availability: for each country set SER at Ethiopia’s level and credit constraint at 0 | |||||
 At Ethiopia’s SER and no credit constraint (C) | 0.109*** (0.024) | 0.285*** (0.010) | 0.033*** (0.006) | 0.119*** (0.062) | 0.031*** (0.017) |
  Chi-square tests A = C | 6.15** | 19.3*** | 2.54 | 11.83*** | 17.93*** |
Panel III: effect of high subsidy with no credit available: for each country set credit constraint at 1 and SER = at Malawi’s level | |||||
 At Malawi’s SER and no credit available (D) | 0.292*** (0.064) | 0.547*** (0.126) | 0.124*** (0.052) | 0.312*** (0.010) | 0.120*** (0.017) |
  Chi-square tests A = D | 3.80* | 5.34* | 2.61 | 20.96*** | 1.63 |