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 off-

farm 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 assumption is that wage and off-

farm labor supply are endogenous

variables that are determined

simultaneously.

The Empirical Model

• 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

non- farm business8.7%

off- farm work26.5%

farm work43%

public & private transfers21.8%

Distribution of Days Working Off- Farm

0%

5%

10%

15%

20%

25%

30%

30 60 90 120 150 180 210 240 270 300 330 360 More

Work days off- farm

Descriptive Statistics- Averages of Main Variables

Participation Equation Participation Equation

Depend. var.: Depend. var.:

Independ. Var. all obs. males females Independ. Var. all obs. males females

Participation in off- farm work (binary)

0.222 0.275 0.176 Completed technicalcollege (dummy)

0.201 0.191 0.210

Age 44.85 44.37 45.33 Completed/ uncompleted higher education (dummy)

0.197 0.200 0.194

Females (dummy) 0.512 Number of plots 2.562 2.576 2.549Number of childrenup to age 6

0.296 0.282 0.304 Total land size (hectare) 1.679 1.623 1.743

Number of childrenin ages 7- 14

0.511 0.481 0.539 Weighted land quality (1-5) 3.159 3.175 3.151

Number of personsfrom age 15 and up 3.627 3.647 3.601

Technical efficiencyin farm production (0-1) 0.216 0.222 0.214

Number of obs.: 7,090 3,299 3,627 Number of obs.: 7,090 3,299 3,627

work off- farm: yes/ nowork off- farm: yes/ no

Results10% 5% Participation Equation Wage Equation Labor Supply equation

Depend. var.:

Independ. Var. all obs. males females all obs. males females all obs. males females

Predicted work days off- farm

-0.003 -0.003 -0.004

Predicted ln(day wage) -0.651 -0.216 -0.444

Females (dummy) -0.104 -0.431 -0.229

Technical college (dummy) 0.121 0.106 0.138 -0.141 -0.006 -0.278

Higher education (dummy) 0.269 0.222 0.318 -0.121 -0.277 -0.014

Number of children 0-6 0.008 0.035 -0.011 -0.034 -0.046 -0.038

Number of children 7-14 0.013 0.033 -0.002 -0.020 -0.029 -0.005

Number of individuals 15+ 0.003 0.006 -0.0002 0.013 -0.006 0.027

Age 0.027 0.029 0.024 -0.003 -0.003 -0.035 -0.005 -0.004 -0.036

(Age)2 -0.0003(45.4)

-0.0003(44.5)

-0.0003(46.8)

0.0003(58.3)

0.0003(60)

Number of obs.: 7,090 3,299 3,627 1,465 841 594 1,577 907 638

work off- farm: yes/ no ln(w) ln(work days off- farm)

Participation Equation Labor Supply equation

Depend. var.: work off- farm: yes/ no

Independ. Var. all obs. males females all obs. males females

Number of plots -0.018 -0.029 -0.011 0.014 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) -0.002 -0.007 0.003 -0.014 0.001 -0.030

Ln(1+private transfers) -0.010 -0.019 -0.003 -0.006 0.006 -0.012

C.V. for production quantities (0-1) -0.050 -0.201 0.015 -0.071 -0.038 0.076C.V. for production prices (0-1) 0.470 0.369 0.332 -1.049 -1.177 -1.146Technical efficiencyin farm production (0-1) -0.229 -0.308 -0.145 0.352 0.479 0.349

Number of obs.: 7,090 3,299 3,627 1,577 907 638

ln(work days off farm)

10% 5%

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

Participation Equation Wage Equation Labor Supply equation

Depend. var.:

Independ. Var. all obs. males females all obs. males females all obs. males females

Participation in off- farm work (binary)

0.222 0.275 0.176

Ln(work days off- farm) 5.208 5.202 5.233

Ln(day wage) 1.109 1.321 0.808

Age 44.85 44.37 45.33 43.58 43.48 43.68 43.18 42.99 43.44

Females (dummy) 0.512 0.405 0.405Completed technicalcollege (dummy) 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

work off- farm: yes/ no ln(w) ln(work days off- farm)

Participation Equation Labor Supply equation

Depend. var.:

Independ. Var. all obs. males females all obs. males females

Number of childrenup to age 6

0.296 0.282 0.304 0.323 0.344 0.292

Number of childrenin ages 7- 14

0.511 0.481 0.539 0.558 0.564 0.544

Number of personsfrom 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) 3.159 3.175 3.151 3.095 3.125 3.057

Technical efficiencyin farm production (0-1) 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

work off- farm: yes/ no ln(work days off farm)