|
Variable
|
Tobit
|
Probit
|
Truncated regression
|
Heckman
|
|---|
|
Coef.
|
t
|
Coef.
|
z
|
Coef.
|
z
|
Coef.
|
z
|
|---|
|
Age
|
0.219
|
1.21
|
− 0.015**
|
− 2.13
|
0.085
|
2.46
|
0.091**
|
2.63
|
|
Age squared
|
− 0.003
|
− 1.54
| | |
− 0.001
|
− 2.38
|
− 0.001**
|
− 2.38
|
|
Female
|
0.358
|
0.39
|
− 0.105
|
− 0.60
|
0.055
|
0.41
|
0.039
|
0.25
|
|
Married
| | |
− 0.403
|
− 1.82*
| | | | |
|
Education attainment
|
0.140
|
0.47
|
− 0.019
|
− 0.28
|
0.147
|
2.63
|
0.157**
|
2.42
|
|
Local market
|
− 0.765
|
− 0.46
|
− 0.308
|
− 0.84
|
0.677
|
2.09
|
0.885**
|
2.31
|
|
Distant market
|
1.873*
|
1.78
|
0.337
|
1.41
|
0.517
|
2.58
|
0.356
|
1.44
|
|
Knowledge
| | |
0.557
|
2.38**
| | | | |
|
Own savings account
|
− 0.858
|
− 1.19
|
− 0.279
|
− 1.72*
|
0.036
|
0.27
|
0.169
|
0.99
|
|
Make decisions on income
| | |
− 0.365
|
− 2.15**
| | | | |
|
ln (Fishing assets)
|
− 0.067
|
− 1.08
| | |
0.022
|
1.84
|
0.024**
|
2.03
|
|
ln (Fish processing assets)
|
− 0.072
|
− 0.96
| | |
0.011
|
0.79
|
0.008
|
0.59
|
|
Madzedze
|
− 0.483
|
− 0.46
|
0.046
|
0.17
|
− 0.802
|
− 4.2
|
− 0.805***
|
− 3.56
|
|
Malembo
|
− 2.359**
|
− 2.24
|
− 0.438
|
− 1.71*
|
− 0.497
|
− 2.57
|
− 0.279
|
− 1.09
|
|
Msaka
|
− 2.154**
|
− 2.00
|
− 0.425
|
− 1.68*
|
− 0.814
|
− 4.08
|
− 0.598**
|
− 2.30
|
|
Chikombe
|
− 1.322
|
− 0.86
|
− 0.360
|
− 0.99
|
− 0.425
|
− 1.51
|
− 0.261
|
− 0.77
|
|
Lambda
| | | | | | |
− 1.128*
|
− 1.87
|
|
Constant
|
4.095
|
1.05
|
1.854
|
3.25***
|
8.054
|
10.44
|
8.240***
|
10.09
|
|
Number of observations
|
382
| |
382
| |
284
| |
382
| |
|
LR/Wald Chi-square
|
25.34**
| |
31.61***
| |
62.17***
| |
51.27***
| |
|
Log likelihood
|
− 1008.58
| |
− 201.712
| |
− 383.964
| | | |
|
Pseudo R2
|
0.0124
| |
0.0752
| | | | | |
- The same probit model has been used as a selection equation in the Heckman selection model and participation equation in the double hurdle model
- ***Denotes statistically significant at 1%
- **Denotes statistically significant at 5%
- *Denotes statistically significant at 10%
