Off-Farm Work Participation of Israeli Farm Couples: the Importance of' Farm

Work Participation Status'

Ayal Kimhi

The 1 I e h r m Ih iumi ty , Rehouot, Israel

This article claims that coeflcients ofoff-farm work participation equati0n.f for farm resrdents might be estimated inconsistently rfselectivity based on farm participation is ignored. Partici- pation equations will be drfferent for farm residents who do not work on farm, especially with respect to the dependence of reservation wages on farm attributes. I estimate an endogenous switching regression model in which farm and ofl-farm participation equa!ions are estimated jointlv while the ofl-farm participation coeflcients are digerent for those who work on farm and those nho don 'I. L'sing the 1981 Israeli Census ofAgriculture data, I rcyect the hypothesis

of insignificant selection bias and the hypothesis ofequal coeflcients in th17 two suhsamples. 1 INTRODUCTION

Empirical off-farm work pmcipation equations of fanners an: often condltioned on farm attnb- utcs. This is because farm attnbutes affect the inarginal value of labor on the farm, which is the relmant resenation wage for off-farm work de- cisions of fanners. The studles of Bollman (1979). Godwinand Marlowe (1990),Lass, Fin- dcisand Hallberg( 1989). SimpsonandKapitany (1983), and Surnner (1982) follow thts line,

However. looking at participation decisions of farm couples. it is not uncommon that one person, mostly the wife but in some cases the husband.doesnot workonthe farmatall. Among lsracli fanncouples. 10percent ofthemen(Tab1e 1) and 59 perccnt of the women (Table 2) I d not work on the farm in 198 1. The dependence of the off-farm participation equation of a person who is not working onthc farmon farmattributes comes only thmugh the f d y budget constrant and not tlmugh the resewation wage. Ths fact inay indicate that the estimated coefficients of the off-farm pamipation equation are inconsis- tent if estimated without condltioning on farm work participation status.2

among others.

In thls paper I suggest an alternative ap- proach, in which farm and off-farm work par- ticipation equations an: estirnated jointly, but the coefficients of the off-farm equation are allowed to m e r for those who work an farm and forthose who don't. In otherwords, the coefficients of the off-farm participation equation are conditioned onthe farm participation stahrs. Th~s is motivated by the observation that off-farm m c i p a t i o n rdtes are quite meren t for farm f d y members who work on the farm and those who do not. As a result, the empirical modd used is an endo- genous switching regression model. The model is estimated separalely for farm hus- bands and wives by maximum likelihood methods using a sample of Israeli farm cou- ples. To establish the advantage of the current model over the commonly used approach of estimating a single off-farm participation equation. I test the hypothesis that the coeffi- cients of the off-farm participation equation are independent of farm work status.

Section 2 of this paper describes the theo- retical framework that leads to the endo- genous switching represenlation of the farm and off-farm participation equations. The data is described in section 3. and the results of

Can J Agnc I c o n 44 481-490 481

4R2 CANADIAN JOIJRNAI, OF AGRICULTIJRAL ECONOMICS

estimating theempirical modelandtestingthe hypothesisare presented in section 4 for men and section 5 for women. Section 6 concludes the paper.

2. THEORETICAL MODEL The model used in th~s paper is adopted from K i d (1994a) with minor simplifications. It assumes utility maximization over consump- tionandleisuresubjectto timeandbudgetcon- straints. Farm couples allocate their time opti- mally between home time, farm work and/or off-farm work. Formally, the optimization problem is:

MAX I/(Th,C;z) (1) ThC.TJTm

~ t . 1. C<x(P;K ,Tf ) + WTm + I 2,Th + T f t T m I T 3. Tf 2 0 4. Tm 2 0.

where Th, Tf and Tm are vectors of time spent on home activities, farm work and off-farm work, respectively, C is household consump- tion, Z is a vector of taste slufters, I is non- earned income, and Wis the vector of off-farm wage rates. x is Lopez’ (1982) comhtional variable profit function. It describes farmprof- its as a function of market prices (0, condi- tional on farm fixed inputs (K) , and family farm labor inputs. The returns to famdy farm labor (WaTf i are assumed to be declining in TI, whle off-farm wages (W) are assumed to be independent of Tm. The advantage of tlus model over other models used in the literature is that it allows family members not to work on the family farm. llus is achieved by includ- ing the non-negativity constraints on the farm workvariables as well as onthe off-farm work va nab le s .

The participation equations are a subset of the Kuhn-Tucker conditions, which are the first order conditions for maximizing the func- tion

U(Th,C;Z) + hfn(P;K, u) + WTm+I-C] f p[T-Tf-Tm-Th] + 0 Tf + 8 Tm ( 2 )

over { C, Tf; Tm, Th} and minimizing it over {h,p,+,S). Given that the first derivatives of utility go to infinity when the respective argu- ments approach zero, the participation equa- tions in farm work and off-farm work respec- tively are:

h / d T f + + / u , = u p , ( 3 ) w+ ari, = u p , (4)

where Ui and U2 are the partial derivatives of utility with respect to the vector of home time and household consumption, respectively. Other necessary conditions imply that Q and 6 are positive if and only d farm work and off- farm work, respectively, are zero. (3) and (4) can in principle be estimated as simultaneous equations, using a version of the Lee and K i d (1993) minimum distance procedure. If, however, the farm participation problem is solved by each individual prior to the off-farm participation problem, farm participation is determined solely by (3). This ordering of de- cisions is absolutely ahitrary, since there are arguments for and against each of the two pos- sible orderings. However, 1 will assume that this is the case throughout this paper.

For those who work on the farm, 8 - 4 x = lJr/U2, and off-farm participation occurs d

all sufficient conditions are met, denotes optimal farm labor supply

given no off-farm work, i.e., Tf solves

h ( P , K , TfilaTf= lJ1 (T- TA X( P;K, Tfi+I;Z) / ~ ~ ( T - ~ J x ( P ; K , Tfl +l;Z) (6)

as long as Tf > 0. For those who do not work on the farm, off-farm participation occurs if:

W h ( P ; K , Tf-)/aTf + d (P,K,T,I,Z)I[J7 (T-Tf *r(P;K,Tf) -tI;Z) (7)

where Tf is the vector of farm labor inputs including a zero element for the person whose participation is considered.

WORKSHOP PROCEEDINGS 483

This leads to the following off-farmpartici-

(8) pation variables:

Y2, * ~2 M7- &<P;K,Tf)BTJ) Y22* 1 u.' - &(P;K, Tf-)/aTf

+ a(P, K, T, 1, z)l U2 (T-Tf-.n(P;K, Tf] + I ; z ) (9)

where participation occurs if the participation variable is positive. It is clear that when speci- fying Y?*as a functionofobservablevariables, the functional form will be dlfferent for those who work on the farm and for those who do not. Adding the farm participation equation

and specifying the observed participation dummies as

dl = 0 otherwise

d21 = 1 iff Y21* > O

d21 = 0 otherwise }iff d l = 0

d22 = 1 iff Y22* > O

d2z = 0 otherwise }iff d l = 1

we get the endogenous switchng regression model described by Maddala (1983. pp. 223).3 I specify the right hand sides of the participa- tion equations as Xi.al + [Ji, X21.a21 + U21, andX2pa22 4- 1/22. respectively (where the X's are vectors of explanatory variables, the a's are coefficients, and the U's are distuhance terms assumed to be jointly normally distrib- ~ t e d ) . ~ Given this notation, the log likelihood function of the model is:

where sumnation is over Observations, pl is the correlation coefficient between UI and IJ2, (i=l,2),Ai=-Xi.ai/oi, A2;=-X2i.a2i/o21,and Azz=-Xzz.a22/o22; 021 and a 2 2 are the stand- a d deviations of U21 and U22, respectively, and 1D is the cumulative distribution function of a standard normal randlm variable.

3. DAT.4 I use data from the 198 1 Census of Agnculture in Israel. Originally, it included 28,526 obser- vations of farm families in moshavim. MoshavimistheHebrew name (inpluralform) for coopelative (or semi-cooperative) villages. In each moshav, membewlup is by family; each of which maintains its own household, farms its own allocation of land and earns its income from what it produces. The moshav is taking advantage of economies of scale by col- lectively handling matters of mutual concern such as purchasing, mark.eting, investments and credt, and operating ;is a member in re- gional and t~ti0~1organii:ations. It is also re- sponsible for education and social activities, and acts as a municipal entity. l h s structure is different from that of the: better-known kib- butz collective^.^ I eliminated those who ex- plicitly defined themselves as non-farming families" (6,281), "private" (as opposed to "family") farms (2,808), and partnerstups (341). These are all exceptional types of ob- servations and I excluded them in order to minimize unnecessary noise. Landless fami- lies and incomplete observations were also ex- cluded.

The finaldataset includes 16,818farmmen and 17,015 farm women observations, and its descriptive statistics appear in tables 1 and 2, respectively. Farm work anrloff-farm workare reported in qualitative ternis. For each of the two sectors, the respondent had to indicate whether he/she is working up to 113 of the time in that sector, up to 213 of .the time, full time, or not at all. The measures of 1/3,2/3 and 'full time' were left forthe respondent's discretion The only limitation was that arespondent can- not report full time work on the farm and off the farm.

The explanatory variabks used includeper- sonal characteristics such as age, years of

4x4 CANADIAN JOIJRNAL OF AGRICULTURAI ECONOMICS

I able ~~ 1 Descriptive ~~ Statistics ~ ~~ Men ~ ~-

~ -~ ~~

I. Explanatory Variables -~ ~-

~

~

Mean (by Farm Partmpauon) Workers Nonworkers

~

~ All-- ~~ _ _ _ _ _ _ ~ _ Vanable

Age 47 3 46 4 55 5 Years m Israela 32 0 31 9 32 5 Years on farm 1 8 7 18 1 23 8 Schoolmg 8 7 9 0 6 2

kam1ly 0- 1 4h 1 6 1 6 1 4

I a ~ ~ ~ i l y 15-21 O 89 0 85 1 2

Family 22-65 1 5 1 4 0 17

F amdy 66+ 0 12 0 1 1 3 0

rota1 land" 3 2 3 2 0 17

Dalry farm 7 8 8 1 5 1

0 88 0 92 0 8 8 ~~ __ Old capital ~- ~ ~

IL Participation ~.

Range IJnits 16-80 Years _ _ _. - __ .

1-80 Years

0-6 1

0-20

0-1 1

0-8

0-9

0-2

0- 8

0- 1

0-3 _ _ . -

Years Years

Persons Persons Persons Persons In(dun. >d percent

lb($81) ~~ ~ .- __-

Total - _ _ - ~~~

Woang oflarm Not workmg Working on farm 5644 (34%) 9430 (56%) 15,074 (90%)

Not workmg 877 (5%) 867 ( so / ) 1,744 (loo/)

- 6521 __ (39%) 10,297 (61%) 16,818 - (100%) I otal a For native Israelis, equal to age ' Number of family members in each age group, excludlng operator

~~ ~ ~

Onginal land allotment 1 dunam = 0 23 acre Norniabve value of capital asets at least ten years old

' I n 1981 prices I actor of exchange 12 39

schooling, and also years in Israel and years on current farm which are available for men only. Also included are the number of family members broken by age groups, and several farm attributes. Farm attributes in the data set included land size broken down by crops and by irrigation status, and livestock by type. Norma- tive values of sales and of value added for each type of farm output were also included (physical quantities were multiplied by average values cal- culated from more decided surveys). Normative values of capital assets were reported by type of

assets and by type of product for which thcy were used (see the note in parentheses above).

The problem with including such farm at- tributes as explanatory variables in an off-farm participation equation is that some or all of them may be endogenous. This is true to the extent that off-farm participation and levels of farming activities are determined jointly. The literature has been mixed about this point (Lass, Findeis and Hallberg, 199 1). While sev- eral researchers used many farm attributes (e.g. Lass et al., 1989), others did not use them

WORKSHOP PROCEEDINGS

Iable 2 Descnptive Stabstics Women

Variable ~

-

Age Schoolmg

Family 0-14”

Family 15-21

Family 22-65

Family 66+

I otal land’

Dairy farm

Old capitald -- __

_ _

~

Working on fann

Not workmg

rota1 _ -

485

All

43 6

8 6

1 6

0 88

2 4

0 23

3 1

0 8

1 0

Workers Nonworkers

43.0

9.4

I .6

7.8

2.2

0.19

3.0

0.9

1.1

44.0

8.1

1.7

0 95

2.4

0.26

3.2

0.7

0.97

Range - . ._-. ___ 14-80

0-20

0-1 1

0-8

0-10

0-3

0-a

0- 1

0-7 3

Units

Years

Years

Persons

Persons

Persons

Persons

In( dunams)c

d-Y lb($8 1 )e

~ IL P a r t i i t i o n - ~- ~-

~ w o r k _ m g f a n n _ ~ p - -- Not workmg _ _ _ - _. rota1 - ~ -

1075 (6%) 5866 (35%) 0,941 (41%)

2580 (1 5%) 7494 (44%) 10,074 (59%)

17,015 (100%) _. ~~ -

(79%) ~~~ -

3 5 5 (2 1 Yo) 13,360 ~ _.

’ Number of family incmbers m each age group hOngmal land allobnent ‘1 dunani = 0 23 acre

Normative value of capital assets at least ten years old Wonnative value of capital assets at least 10 years of age ‘ In 1981 prices

at all (e.g. Huffman and Lange, 1989). I follow the approach of Sumner (1982), and try to use those farm attributes which are more likely to be exogenous to the off-farm participation de- cision (it is important to include at least several farm attributes since input and output price data is not available).

In particular. I use the farm’s original land allotment, the value of capital assets whch were purchased or built at least ten years prior to the survey. and whether the farm includes a dairy operation. The land variable is appropri- ate because of the unique institutional arrange- ments governing the behavior of these farm residents (Kimlu, 1991~). Specrfically, land was equally distributed among residents in

each village at the time of establishment of the village. Farm owners are not allowed to buy or sell land (with the excleption of selling the wholc farm and moving out of the village). Land rentals are illegal, although short time rentals exist in practice. However, land rentals are not included in the land variable used in ths analysis. Therefore, current time alloca- tion decisions cannot affect the land variable. Obviously, the same is true for the old capital stock variable (whch is highly correlated with total capital stock).

The dairy dummy was chosen because of two reasons. First, labor requirements of a dairy operation are much different from those of other farm activities. H:ence, dairy farming

486 CANADIAN JOIJRNAL OF AGRICIJLTIJRAI, ECONOMICS

activity information is important for studies of off-farm participation more than information on other farm activities. Second. entries into and exits from dairy farming have been rela- tively rareinIsrael, inpartbecauseofagnculhml policy (milk production has been heavily subsi- d u d over the years, and hence was subject to stnct quotas). Also. b r y farming involves pro- hibitively large capital investments, especially for farmers without sufficient collateral for rais- ing debt.

4. RESULTS FOR MEN The estimation results are presented in tables 3 and 4 for men and women, respectively. Estima- tion was conducted with the MAXLIK applica- tion of GAUSS, using a two-stage estimation procedure to produce startlng values (KI~~I, 1991a). For two th~rds of the men worlung on the farm, off-farm pamcipation was correctly p d c t e d (a correct p d m o n means that the probability of parhcipation is greater than one half for those who participate and less than one half for those who don‘t). The percent of c o m t pdc t ions was close to 80 percent among men who did not work on the farm. The conelation coefficient is stawtically s ipfkant only in the equation of farm workers, indicatmg that the er- ror term of the men’s off-farm equation is drawn after the farm participabon decision has been made (Poirier and Ruud, 1981).

The main hypothesis that I test is that the co- efficients are equal in the two subsamples. A for- mal likehhood ratio test rejects the hypothesis in all reasonable sigrufcance levels (hkelihood ra- tio statistic of 500 with 17 degrees of free- dom). The conclusion is that estimating a single off-farm work participation equation over the whole sample results in incorrect es- timators. Another hypothesis is that the coef- ficients of farm attributes are zero in the subsample of those who don’t work on the farm. The likelihood ratio statistic for this hy- pothesis is 9.32. with 3 degrees of freedom. Therefore, the hypothesis can be rejected at the 5 percent si@icance level, but not at the 1 per- cent level @-value t 2.5 percent). However, the change in the other coefficients after excludmg farm attnhtes is margnal, and so is the drop in the percent of correct pdctions. On the other

hand, farm attributes are hghly sigruficant in the equation of those who work on the farm (likelihood ratio statistic of 6 10 for a similar exclusion hypothesis).

PersonaI characteristics have the expected effects on men’s off-farm participation (Lass et al., 1991). Note that these are reduced-form effects, in the sense that the coefficients meas- ure the effects of the explanatory variables on the difference between the off-farm wage rate and the reservation wage. Off-farm participa- tion is first increasing with age and then de- creasing. Itpeaksaroundtheageof43 forthose who work on farm and around 25 for those who don’t. Schooling and years in Israel in- crease off-farm work participation, more so for those who don’t work on the farm. Both are measures of general human capital. Years on the farm decrease off-farm work participation only forthose who don’t work on the farm. We would have expected this effect to be stronger in the other subsample. However, farm work in the past is not known, and perhaps those who don’t work on the farm had done so in the past. The equations included also dummy vari- ables for ethnic origin (not reported in table 3). Among those who work on farm, those born in foreign countries were more likely to work off the farm than native Israelis. The origm dummies were not SigIUficant in the subsample of those who don’t work on the farm.

The number of family members indifferent age groups increase off-farm participation, thoughonly some of the effects are statistically significant. This is probably because other family members can substitute for operator’s labor both on the farm and in house work (Kimlu, 1994b). All three farm attributes in- cluded (land holdings, old capital stock and the daixy dummy) S e c t off-farm participation negatively, probably because they all contrib- ute to farm labor productivity.

5 . RESULTS FOR WOMEN Table 4 presents the results of the farm and off-farm participation equations of women. Besides the variables shown in the table, the farm participation equation also included a set of regional dummies and a set of village estab- lishment year dummies.

WORKSHOP PROCEEDINGS 487

Work on farm Don’t work on farm

Intercept 2.486 -2.305 0.290 (1 5.9)** (-1 1.3)** (0.26)

Age -0.025 0.1 12 0.056 (-13.5) (12.2)** (1.77)

(Age)’/100 -0.136 -0.1 18 (- 14.8)* -(4.21)**

In Israel 0.004 0.008 0.014 ( I . 7 7 ) * (4.53)* * (1.96)

Years on farm -0.003 -0.001 -0.012 (-1.25) (-0.3 5 ) (- 1.87).

Schooling 0.015 0.038 0.081 (3.7 1 )* * (10.7)** (8.66)**

Family 0-14 -0.044 0.002 -0.015 (-4.28)** (0.24) (0.52)

Farmly 15-21 0.019 -0.037 0.028 (1 3%) (3.24)** (0.95)

Family 22-65 -0.198 -0.024 0.072

(-15.6)** (1.40) (1.07) Family 66+ -0.099 0.023 0.138

(-2.61)** (0.65) (1.21) Total land 0.077 -0.277 -0.121

(4.1 5)** (1 3.5)** (2.28)* Old capital 0.016 -0.016 0.005

(2.90)* (3.24)** (0.34) Diury dummy 0.426 -0.520 0.051

(7.05)** (9.13)** (0.25) Correlation -0.641 -0.248

(3.59)** (0.78) No. of cases 16,818 15,074 1,744

Log llklihood Notes: All equations included a set of e h c origin dummies. Farm participation included a set of establishment year dummies as well. Asymptotic t-statistics m parenthesis. * significant at the 5% level; ** significant at the 1 % level. %of correct predictions: pre&ction=l (0) if the probability of Y=l is greater (smaller) than 10.

% correct pred 66.6 79.7 -9079 -792 ______________ -4875 .~

48 8 CANADIAN JOLJRNAL OF AGRICIJLTURAL ECONOMICS

_ _ _ _ _ ~ _ _ _ _ - Table 4 Participation resvlts Women ~ ~~~ - -

off-farm ~ -~ - - - _ _ Farm

Work on farm Don't work on farm - .. __ - ~~

Intercept -2 07 -2 52 -3 14 (-13.0) (-6.1) (-17.0)

Age 0.85 0.85 0.078

(13.0) (5 .0) (8.6) Age squared -0.0009 -0.0012 -0.0012

Schoolmg

Family 0- 14

Family 15-21

Family 22-65

Family 66+

Land

IIalIy farm

Old capital'

Correlation

(-13.0) 0.036 (13.0) -0.022 (-2.9) -0.045 (-4.5) -0.104 (-9.8) -0.095

(-6.2) 0.078

( 1 1 0)

(4 1 )

-0 065

0.014 (0.66) 0.024

(1 0) 0.181

(-1 1.0)

(23.)

(-9.0) -0.019

(-1.3)

0.087

-0.090

0.043

(2-91 0.157

(-3.9) (3 6) (4.7)

(-3.1) (-3.8) (6 2)

-0 039 -0.098 0091

0.340 -0.449 -0.177 (-5.1) 0.005 (0.42) -0.270

(-3.3)

(-4.9) -0.035

-0.925 - (-2 1) (-3 7)

N O E S asymptohc T-stahshcs m parentheses The farm equahon also included sets of regional dummies and village establishment year dummies 'Land and capital stock were measured m natural loganthms to mmmize the effects of outliers Normallzahon was used such that a zero remamed a zero

One can see that the age profiles of farm participation are concave as expected, with participation probability peaking around the age of 47. Schooling has a positive and sigrufi- cant effect. The number of other family mem- bers inall age groups affects farm participation negatively, with adults having a greater effect than children. In dairy farms, farm participa- tion of farm women is much higher, perhaps because dairy farm work is a good complement

to housework. Land size affects participation negatively: in larger farms, women have a lower tendency to wok on the farm. It could be that lured labor substitutes for family mem- bers in larger farms, and the income effect may play a role here too. In contrast, capital stock has a positive effect on farm participation.

Comparing the off-farmparticipationequa- tions for the two subsamples of farm women, we first notice that the correlation coefficient

WORKSHOP PK0CE;EI)INGS 489

between the stochastic terms of the farm and off-farm participation equations is close to mi- nus one in thc equation of farm nonworkers, in contrast to a much smaller (in absolute value) correlation coefficient for farm work- ers. This is thc opposite of what was found for men (Table 3).

The coefficients of personal characteristics in the two subsamples are not signlficantly dif- ferent. Off-farm parmipation probability as a function of age peaks slightly later for farm workers (at the age of 35 versus 3 3 for non- workers). and in both cases off-farm partici- pation probability peaks much earlier than farm participation probability (at the age of 47). and declines much faster afterwards. The schooling coefficient is positive and signifi- cant. and is approximately twice as large as the schooling coeffcicnt in the farm participation equation. which means that schooling, at least as measured hcre. contributes more to off-farm earnings than to farm productivity. These re- sults are in line with those of others (Lass ef 01.. 199 1). esccpt that age profiles of off-farm participation probability are more concave and peak earlier than in the other studies. The num- ber of children decreases off-farm participa- tion probability. and the number of adults increases it. in both subsamples. These effects are stronger in the nonworkers equation (with the exception of farnilq- rncmbers over 65 years of age).

The major difference between the two sub- samples lies in the cocfficients of farm attrib- uks. The land s i x variable llas a negative coefficient for fann tvorkcrs and a positive one for nonworkers. TIC dairy farm dummy has a negative cocfficient in both cases. wluch is much larger in absolute value for farm work- ers. Capital stock has a negative and signifi- cant coefficient in the nonworkers equation, and a p0sith.c and non-significant coefficient in the workers equation. These differences are expected (Kimhi. 1 99lb). since for those who work on the fann. farm attributes affect farm labor demand and hence affect off-farm labor supply through the time consmnt . For those w ho do not work on the farm. the effect is only through the budget constraint (i.e., both sub- stitution and income effects exist for farm

workers, while only the income effect exists for nonworkers). It is evident that the substi- tution and income effect!; work in the same direction in the workers’ equation, since in daity farms (when: family labor demand is relatively high), farm women have a higher tendency to work on the fann. This last finding is in line with the men’s participation results discussed earlier (Table ?,). I do not have an explanation for the positive land coefficient in the nonworkers equation.

Finally, an attempt WES made to evaluate the unconditional marginal effects of the ex- planatory variables on the latent off-farm par- ticipation patterns of farm women (Huang. Raunikar and Misra, 1991; Kirnlu, 1992), rather than the partial effect represented by the estimated coefficients. Thc results are qualita- tively unchanged with respect to personal and family characteristics. Land size has a small positive effect on the marginal off-farm par- ticipation tendency, in thi:; case.

6. SUMMARY AND CONCLUSIONS In tlus article I claim that estimating off-farm work pmcipation equations of f m i e r s may produce biased estimates if one does not con- trol for selection on the basis of farm work participation. I argue that resenation wages of those who wouldn‘t work on the farm even if off-farm employment was not available, and of those who would, have different functional f o m . As a result. I suggst estimating farm and off-fann participatio,n equations jointly using an endogenous switching regression model.

I estimate such a model using Israeli data. and strongly reject the hyl~theses that selec- tion is unimportant and that the off-farm par- ticipation equations’ coeflicients are equal in the two groups of farmers. This supports my suggestion that off-fann work participation equations should be conditioned on the farm work paficipation dummj in empirical appli- cations, and that the two participation deci- sions should bc jointly analyzed.

NOTES ’Part of this research was conducted was I while visiting at the Department of Agncultural and Kc-

490 CANADIAN JOURNAL OF AGRICULTURAL ECONOMICS

source Economics, University of Maryland. The assistance of Haim Regev and Meir Rothchild from the Central Bureau of Statistics in Israel is espe- cially acknowledged. 2To be more precise, h s is only true for farm residents who wouldn’t supply labor to the farm even if off-fann work was not available. 3This is a special case of Heckman’s ( 1978) simul- taneous equation system specification. ’hese are in fact first-order approximations plus the approximation errors. ’In the latter, the family has no economic or social role; membership is individual, all property is com- munity-owned, and work and consumption are equally shared by all members.

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