Off-farm labour participation of farmers and spouses Alessandro Corsi University of Turin.

Off-farm labour participation of Off-farm labour participation of farmers and spousesfarmers and spouses

Alessandro Corsi

University of Turin

The problemThe problem

Off-farm work is widespreadIt helps the adjustment process of

farmers to new market conditionsIt is important to analyse the

variables that influence the choice of working off the farm

Theoretical modelTheoretical model

Off-farm work participation is a dichotomous variable (may be yes or no)

The farmer chooses to work off the farm if the market wage is larger than the reservation wage (= the minimum wage for which he is willing to work off the farm)


w*

O family labour

inco

me


O

inco

me

family labour


w

O

inco

me

family labour


The reservation wage therefore depends on:

personal characteristics (age, sex, education, etc.)

household characteristics (e.g., number of children)

farm characteristics (size, farming system, etc.)


The market wage depends on: personal characteristics (age, sex, education, etc) characteristics of the labour market


The farmer will have an off-farm job if:

market wage > reservation wage

W > W*


w

w*

O family labour

inco

me


The market wage can be written:

iLtPiwi '2

'10

The reservation wage can be written:

iH tFiPiwi *'3

*'2

*'10

*


The difference between the market and the reservation wage, w - w* is :

ii

H iFiPio

LiPioyi

'3

'2

'1

'2

'1


For brevity, y*can be written as:

iX iyi

'

(X are all the explanatory variables, and

is the random term)

Theoretical modelTheoretical model y* cannot be observed; it can only be observed if the farmer works off the

farm or not. Then:

Pr[off-farm work] = Pr[y* > 0] =

= Pr[’X < ] = [’X]

( is the cumulative probability of the random variable , assumed to be normal)


X

Prob[ < X]=Prob[off-farm job]

Pro

b[

]


The parameters of the equation can be estimated through a probit model It yields the probability of the outcome (off-farm yes or no) as a function

of the explanatory variables It is also possible to estimate the change in probability resulting from a

change in the explanatory variable (marginal effect)

DataData

351 farms in Pennsylvania surveyed in 1985 and again in 1991

351 farm operators 344 spouses

DataData

data on personal characteristics: age, sex, education

data on household characteristics: # children of different age

DataData

data on farm characteristics: farm size principal farm enterprise (dairy, other labour

intensive, all-year-round or seasonal - dummy variables)

DataData

characteristics of the labour market employment share by sector ratio of average nonfarm to farm incomes unemployment rate

ResultsResults

Models estimated for operators and spouses:

fitting results comment

ResultsResults

Observations: 351Log-Likelihood -163.002Log-Likelihood (slopes=0) -229.142LR test of the model: 2

(d.f.)132.279

(15)Correct predictions (%):Total 78.9Not working off the farm 87.6Working off the farm 63.5

Operators

ResultsResultsOperators

Variables Coeff. t-values Partialderivatives

Constant -10.462 -3.57AGE 0.294 3.73 0.103AGE2 -0.003 -4.12 -0.001EDUC 0.099 2.68 0.035CHILD<5 0.020 0.07 0.007CHILD<17 0.066 0.75 0.023CHILD<30 -0.007 -0.06 -0.002ACRES -0.001 -3.78 -0.00048DAIRY -1.426 -7.03 -0.500LABINT -0.639 -1.89 -0.224SEASINT -0.449 -1.52 -0.158TRADE -0.183 -3.00 -0.643HIGHSERV 0.447 2.59 0.157LOWSERV -0.002 -0.05 -0.0007MANUFACT 0.036 1.70 0.013INCOMGAP 3.001 2.91 1.054

ResultsResults

FOR OPERATORS: Personal characteristics have a significant impact

on off-farm labour participation The same is true for farm characteristics and

labour market characteristics Household characteristics do not significantly

affect operators’ choices

ResultsResultsSpouses

Observations: 334Log-Likelihood -186.797Log-Likelihood (slopes=0) -220.834LR test of the model: 2

(d.f.)68.074

(15)Correct predictions (%):Total 71.0Not working off the farm 85.2Working off the farm 47.2

ResultsResultsSpouses

Variables Coeff. t-values Partialderivatives

Constant -6.895 -2.60AGE 0.085 1.17 0.315AGE2 -0.001 -1.80 -0.05EDUC 0.193 5.13 -0.071CHILD<5 -0.549 -1.98 -0.204CHILD<17 -0.144 -1.67 -0.053CHILD<30 -0.102 -0.88 -0.038ACRES -0.000 -1.38 -0.0016DAIRY -0.199 -1.13 -0.074LABINT -0.011 -0.03 -0.004SEASINT -0.473 -1.60 -0.175TRADE 0.049 0.87 0.181HIGHSERV 0.211 1.35 0.078LOWSERV 0.009 0.26 0.003MANUFACT 0.043 2.20 0.016INCOMGAP 0.117 0.12 0.043

ResultsResults

FOR SPOUSES: Among personal characteristics, only education has a

significant impact on off-farm labour participation Household characteristics, particularly small children,

significantly affect spouses’ choices Farm characteristics have no influence Among labour market characteristics, only low-wage

manufacturing employment increases the probability of off-farm work

ResultsResults Further results can be drawn from more sophisticated

econometric methods by using data from both surveys Farmers and spouses who choose an off-farm work in the

past are more likely to make the same choice in the following For farmers, this is most likely because when they started an

off-farm work they modified the farm, so that it is not easy to come back

For spouses, this is most likely because they accumulated work experience, and hence, have higher market wages.

ConclusionsConclusions This is an example of how econometric methods can

be used to assess empirical questions The results are consistent with the theory, but more

detail has been gained It is possible to make predictions of what will happen if

some explanatory variable will change It is possible to detail these effects for farmers and

spouses (who exhibit different behaviour), for small and large farms, etc.

THANK YOU FOR YOUR ATTENTION!

Off-farm labour participation of farmers and spouses Alessandro Corsi University of Turin.

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