Off- Farm Labor Supply of
Farm- Families in Rural
Georgia
Dr. Ayal Kimhi
Ofir Hoyman
Tbilisi, 2005
Research Goals
Estimating the factors affecting the labor
supply of Georgian family members off-
farm by focusing on:
1. Personal characteristics.
2. Farm characteristics.
3. Having official document owning the
land.
4. Financial risk in farm work.
5. Efficiency in managing the farm.
6. Wage from off- farm- work.
7. Other incomes (not from work).
The Conceptual Model
• The family members decide
simultaneously on consumption and
leisure together with farm production
and time allocation to farm and off-
farm for each family member.
• The farm family maximizes utility under
time constraint; budget constraint and
a farm production function.
• The internal solution: each family
member equates his marginal value
of time in farm work, leisure and offfarm work.
• A member of the farm family will not
participate in the local labor market if
the wage he could earn is lower than
the marginal value of his work on the
farm at zero off- farm work hours.
The Empirical Model
• The assumption is that wage and offfarm labor supply are endogenous
variables that are determined
simultaneously.
• The estimation is a four-step procedure
and based on the Sample Selection
Model.
(A) Estimating the participation equation
and deriving predicted Inverse Mill’s
Ratio;
(B) Estimating the reduced-form labor
supply equation and deriving
predicted off- farm work days;
(C) Estimating the wage equation after
substituting predicted off-farm work
days and deriving predicted wage;
(D) Estimating the structural labor
supply function after substituting
predicted wage.
Data
• The data used were collected by the
Individual Farm Owners’ Survey carried
out on 2003 in four rural districts of
Georgia: Dusheti, Mtskheta, Sagarejo,
and Gardabani.
• The survey included 2,520 individual
farms; 630 farms from each district.
• There are 7,090 individuals in the
sample older than 14.
• 1,577 (22%) individuals are working
off the farm.
Devision of average rural family income by
sources
public & private transfers
21.8%
farm work
43%
non- farm business
8.7%
off- farm work
26.5%
Distribution of Days Working Off- Farm
30%
25%
20%
15%
10%
5%
0%
30
60
90
120
150
180
210
240
Work days off- farm
270
300
330
360
More
Descriptive Statistics- Averages of Main Variables
Participation Equation
Participation Equation
Depend. var.:
work off- farm: yes/ no
Depend. var.:
work off- farm: yes/ no
Independ. Var.
all obs. males females
Independ. Var.
all obs. males females
Participation in offfarm work (binary)
0.222
0.275
Age
44.85
44.37
Females (dummy)
Number of children
up to age 6
Number of children
in ages 7- 14
Number of persons
from age 15 and up
0.512
Number of obs.:
Completed technical
college (dummy)
Completed/ uncompleted
45.33
higher education (dummy)
Number of plots
0.176
0.201 0.191
0.210
0.197 0.200
0.194
2.562 2.576
2.549
0.296
0.282
0.304 Total land size (hectare)
1.679 1.623
1.743
0.511
0.481
0.539 Weighted land quality (1-5)
3.159 3.175
3.151
3.627
3.647
Technical efficiency
3.601 in farm production (0-1)
0.216 0.222
0.214
7,090
3,299
3,627 Number of obs.:
7,090 3,299
3,627
Results
10%
5%
Depend. var.:
Independ. Var.
Predicted work days
off- farm
Participation Equation
Wage Equation
Labor Supply equation
work off- farm: yes/ no
ln(w)
ln(work days off- farm)
all obs. males females all obs. males females all obs. males females
-0.003 -0.003
-0.004
Predicted ln(day wage)
-0.444
-0.034 -0.046
-0.038
-0.104
0.121
0.269
0.008
0.106
0.222
0.035
0.138
0.318
-0.011
Number of children 7-14
Number of individuals 15+
0.013
0.033
-0.002
-0.020 -0.029
-0.005
0.003
0.006 -0.0002
0.013 -0.006
0.027
Age
0.027
0.029
-0.005 -0.004
-0.036
Females (dummy)
Technical college (dummy)
Higher education (dummy)
Number of children 0-6
2
(Age)
Number of obs.:
0.024
-0.431
-0.141 -0.006
-0.121 -0.277
-0.651 -0.216
-0.229
-0.003 -0.003
-0.0003 -0.0003 -0.0003
(45.4) (44.5) (46.8)
7,090
3,299
3,627
-0.278
-0.014
-0.035
0.0003
(58.3)
1,465
841
594
0.0003
(60)
1,577
907
638
10%
5%
Depend. var.:
Independ. Var.
Participation Equation
Labor Supply equation
work off- farm: yes/ no
ln(work days off farm)
all obs. males females all obs. males females
Number of plots
-0.018
0.003
0.042
Total land size (hectare)
0.00001 -0.001 -0.0003 -0.0001 0.002
-0.001
Weighted land quality (1-5)
-0.015
-0.030
-0.011
0.011
0.0002
0.017
Land document (dummy)
-0.024
-0.049
0.003
-0.014
0.038
-0.049
Ln(1+public transfers)
Ln(1+private transfers)
-0.002
-0.010
-0.050
-0.007
-0.019
-0.201
0.003
-0.003
0.015
-0.014
-0.006
-0.071
0.001
0.006
-0.038
-0.030
-0.012
0.076
0.470
0.369
0.332
-1.049
-1.177
-1.146
-0.229
-0.308
-0.145
0.352
0.479
0.349
7,090
3,299
1,577
907
C.V. for production quantities (0-1)
C.V. for production prices (0-1)
Technical efficiency
in farm production (0-1)
Number of obs.:
-0.029
-0.011
3,627
0.014
638
Conclusions
• Farmers use the off-farm labor
market to supplement farm income.
• Off-farm income compensates
farmers for the income risk they
face in farming.
• The results indicate that off-farm
labor market is in the early stages of
development:
 the returns to human capital seem to
be nonexistent relative to the returns
to physical strength.
 wages in part-time (temporary or
seasonal) off-farm work surpass the
wages in full-time jobs.
 the opportunities for females are
much lower than those for males.
• The off-farm labor decisions are
sensitive to the situation in the land
market:
 possession of a land document
decreases off-farm labor
participation, indicating that a land
document increases farmers’
confidence in their ability to make a
living out of farming and therefore
reduce their tendency to seek
alternative income sources.
 the farm efficiency has a negative
effect on the probability of working off
the farm, but has a positive effect on
days of work off the farm. This could
indicate that farmers have difficulties
expanding their farming operation.
 the difficulties to expand farm
operation can be a consequence of
constraints on land transactions, credit
rationing, or other constraints.
Thank you
for
listening
Depend. var.:
Independ. Var.
Participation in off- farm
work (binary)
Participation Equation
Wage Equation
Labor Supply equation
work off- farm: yes/ no
ln(w)
ln(work days off- farm)
all obs. males females all obs. males females all obs. males females
0.222 0.275
0.176
Ln(work days off- farm)
Ln(day wage)
Age
44.85 44.37
Females (dummy)
Completed technical
college (dummy)
0.512
45.33
1.109 1.321
0.808
43.58 43.48
43.68
0.405
5.208 5.202
5.233
43.18 42.99
43.44
0.405
0.201 0.191
0.210
0.260 0.234
0.300
Completed/ uncompleted
higher education
(dummy)
0.197 0.200
0.194
0.367 0.305
0.457
Number of obs.:
7,090 3,299
3,627
1,465
841
594
1,577
907
638
Depend. var.:
Independ. Var.
Participation Equation
Labor Supply equation
work off- farm: yes/ no
ln(work days off farm)
all obs. males females all obs. males females
Number of children
up to age 6
0.296
0.282
0.304
0.323
0.344
0.292
Number of children
in ages 7- 14
0.511
0.481
0.539
0.558
0.564
0.544
Number of persons
from age 15 and up
3.627
3.647
3.601
3.661
3.660
3.665
Number of plots
2.562
2.576
2.549
2.673
2.622
2.741
Total land size (hectare)
1.679
1.623
1.743
2.261
1.781
2.990
Weighted land quality (1-5)
Technical efficiency
in farm production (0-1)
3.159
3.175
3.151
3.095
3.125
3.057
0.216
0.222
0.214
0.180
0.195
0.162
Number of obs.:
7,090
3,299
3,627
1,577
907
638