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

2. The problem Off-farm work is widespread It helps the adjustment process of farmers to new market conditions It is important to analyse the variables that influence the choice of working off the farm

3. Theoretical 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)

4. Theoretical model

7. 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.)

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

9. Theoretical model The farmer will have an off-farm job if: market wage > reservation wage W > W*

10. Theoretical model

11. The market wage can be written: The reservation wage can be written:

12. Theoretical model The difference between the market and the reservation wage, w - w* is :

13. Theoretical model For brevity, y*can be written as: (X are all the explanatory variables, and  is the random term)

14. Theoretical 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)

15. Theoretical model

16. 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)

17. Data 351 farms in Pennsylvania surveyed in 1985 and again in 1991 351 farm operators 344 spouses

18. Data data on personal characteristics: age, sex, education data on household characteristics: # children of different age

19. Data data on farm characteristics: farm size principal farm enterprise (dairy, other labour intensive, all-year-round or seasonal - dummy variables)

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

21. Results Models estimated for operators and spouses: fitting results comment

22. Results Operators

23. Results

24. Results 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

25. Results Spouses

26. Results

27. Results 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

28. Results 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.

29. Conclusions 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.

30. THANK YOU FOR YOUR ATTENTION!