off-farm labor decisions by ontario swine producers

Off-farm Labor Decisions by Ontario Swine Producers

Alfons Weersink Assistant professor, Department of Agricultural Economics and Business,

University of Guelph, Guelph, Ontario.

Received 19 August 1991, accepted 14 May I992

The off-farm employment decisions of Ontario swine producers are examined using household production theory. It is determined that human capital and farm characteristics have the largest impact on off-farm labor participation. Increases in education are found to have a larger effect on market earnings than on the marginal value productivity of farm labor, thereby increasing the probability of off-farm employment. The chances are also found to increase with an increase in relative financial obligations as measured by the debt-to- equity ratio. Years of farming experience and farm size have a negative impact on the probability of working off-farm due to their positive relationship with marginal farm labor productivity. The results indicate that the trend to a bimodal farm size distribution will likely continue in the Ontario hog industry. Economic policies designed to increase family farm income may help weather fluctuations in farm income but will have little impact on off-farm employment decisions.

Les dtcisions de rechercher un emploi ti l’exttrieur chez les producteurs de porcs de 1’0n- tario ont ttC examinees selon la thtorie de la production des mtnages. On a constatt que c’est le capital humain et les caracttristiques de I’exploitation qui exercent l’effet le plus fort sur le recours au travail exttrieur. L’CICvation du niveau de scolariti avait plus d’effet sur les gains obtenus B I’extCrieur que sur la productivitt de la main-d’oeuvre de I’exploitation, augmentant ainsi d’autant la probabilitt de rechercher un emploi exttrieur. Ces chances augmentaient aussi avec l’accroissement des obligations financikres relatives, mesurkes par le ratio dette-avoir propre. Le nombre d’annCes d’expkrience en exploitation et la taille de I’entreprise avaient un effet ntgatif sur la prohahilit6 de travailler a I’exttrieur, B cause de leur association positive avec la productivitk marginale de la main- d’oeuvre de l’exploitation. Ces rksultats indiquent que la tendance B une distribution bimo- dale de la taille des exploitations devrait se poursuivre dans le secteur de I’tlevage du porc en Ontario. Les politiques Cconomiques destintes a amdiorer le revenu des fermes familiales devraient les aider a surmonter les periodes de creux, mais elles n’auront que peu d’influence sur la dtcision de chercher un emploi a l’exttrieur.

Canadian Journal of Agricultural Economics 40 (1992) 235-251 235

236 CANADIAN JOURNAL OF AGRICULTURAL ECONOMICS

INTRODUCTION Productivity of agricultural labor has grown at a faster rate than the demand for agricultural produce, resulting in a large reallocation of labor from farm to non- farm markets. The proportion of the labor force in agriculture has fallen from approximately 20% in 1950 to 2.3% for Ontario and 3.4% in Canada in 1989. Those who remain employed in agriculture are more likely in comparison with previous years to earn off-farm income. For example, the percentage of farm operators with wages and salaries from non-farm sources has risen from 46% in 1973 to 60% in 1988. In conjunction with the increased number of farmers working off the farm is the increased importance of the income earned from these other sources. Not only have the absolute dollar amounts increased steadily but also the percentage of total net income attributable to off-farm wages and salaries has risen significantly for the average farm tax filer in Ontario from approximately 60% to 90% over the past two decades.

The increasing importance of off-farm income to agricultural producers may be a result of the increased financial obligations in agriculture (Simpson and Kapitany 1983). Gunter and McNamara (1990) note the use of off-farm income by farm families to survive the downturns in the agricultural economy while Steeves (1979) attributes the increased percentage of days worked off the farm by new entrants in Canadian agriculture as a means to handle the increasing capital investments required to start farming. The decision to work off the farm can also be explained by neoclassical labor supply theory. This framework has been used in studies by Bollman (1979), Gould and Saupe (1989), Huffman (1980), Huffman and Lange (1989), Lass, Fiiideis and Hallberg (1989), Simpson and Kapitany (1983), and Sumner (1982). According to this theory, off-farm work increases when the marginal return from off-farm work becomes greater than the marginal returns to farming. Thus, changes in relative returns and improved human capital skills can also explain the increasing importance of outside employment.

There are several reasons why understanding the off-farm work decisions by agricultural producers may be of concern. The first is that it may provide insights into the future structure of the agricultural sector. The trend to a bimodal distribution in farm size would be supported if generally it is the middle age cohort of producers who are most likely working off the farm rather than beginning farmers. The second reason is related to the rural development issues of part-time farmers discussed by Bollman (1979). A policy goal of increasing farm income may be accomplished by increasing off-farm job opportunities and may also slow, rather than speed, the exodus of farmers by offering stability in farm family income. A final reason for understanding who is working off the farm and why is to ensure that extension efforts may be targeted appropriately. The extension needs and desires will be different for off-farm workers if they are generally new entrants to farming versus established producers running a small farming operation.

OFF-FARM LABOR DECISIONS 237

The purpose of this paper is to examine the off-farm labor decisions of Ontario swine producers. The paper begins with the development of a theoretical household production model based on neoclassical labor supply theory. The model is then modified to account for the financial obligations of farm operators and its effect on labor allocation decisions. The next part of the paper presents the variables used in the model, along with a description of the survey used to acquire these variables. The empirical results are then presented, followed by the implications the findings have for policy makers and extension leaders.

THEORETICAL MODEL The decision to work off the farm is modeled using household production theory. The early work in this area, which combines producer and consumer theory, was by Becker (1965) and is presented along with a review of empirical applications in Deaton and Muellbauer (1985), Singh, Squire and Strauss (1986) and more recently by Juster and Stafford (1991). In contrast to the neoclassical work-leisure choice model, which focuses on differences in the shape of individual utility func- tions between income and leisure, the household production function approach considers both technical factors of production and time costs (Gunderson and Riddell 1988).

In the household production model, the household maximizes utility by con- suming various commodities it produces by combining market goods and time. Utility, U , is derived from purchased goods, G, and leisure, L, and is affected by environmental factors, E , such as age, which are assumed to be exogenous to current consumption decisions:

U = U ( G , L ; E ) ( 1 )

Utility is maximized subject to constraints on time, income and farm productivity. The farmer has a fixed amount of time, T, which can be allocated to either leisure or work, which consists of time spent on the farm, F, plus the hours spent on off-farm work, OF. Thus the time constraint can be expressed as:

T = L + F + OF ( 2 )

The consumption of market goods at the price PG is limited by the amount of available income earned from farm profits, off-farm wages, and other exogenous household income, V . Farm profit is equal to the price of farm output, P , multi- plied by output, Q, less the variable costs of production, RX, where R is the input price vector and Xis the quantity of inputs used. Off-farm income is the product of the wage rate, W, and the hours worked off-farm, OF. The budget constraint is therefore:


The technology available to produce farm output represents the final constraint to the household:

where f ( . ) = a strictly concave production function,

K = a vector of fixed farm inputs and H = a vector of human capital stock variables influencing the produc-

These same human capital variables will also influence the off-farm earning poten- tial of the farmer along with other market conditions, M , which implies that the wage rate should be expressed as:

tivity of farm.

W = W ( H , M ) ( 5 )

Substituting Eqs. 2, 3 , 4 and 5 into Eq. 1 results in the following utility function, which the farmers maximize through the choice of variable inputs X and allocation of labor to farm, F, and off-farm activities, OF:

M a x U [ P ’ - f ( F , X ; K , H ) - R ‘ X + W ’ ( H , M ) - O F + V ‘ , T - F - O F ; E ]

X , F, OF (6)

where the prime notation denotes monetary variables deflated by the price of consumption goods, (e.g., P‘ = PIPG). The associated Kuhn-Tucker conditions for a maximum are:

where UG and the U, are the marginal utility of consumption and leisure andf, and f F are the marginal productivity of variable inputs and farm labor, respectively.


The first-order conditions given by Eq. 7 state that variable inputs will be used to the point at which their marginal value product is equal to their marginal cost. An input will not be used if the marginal cost is greater than its marginal return. It is assumed that the farmers spend a positive amount of time on the farm ( F > 0), so Eq. E, holds as an equality. The producer will allocate hours to the farm up to the point that the marginal rate of substitution between leisure and consumption, ULIUG, is equal to the marginal value of farm labor, P' * f F ( K , H ) . Using Eq. 9, off-farm work will be zero (OF = 0) if the marginal return to off- farm labor or wage rate is less than the marginal rate of substitution between leisure and consumption goods (W'(H, M) < U L / U G ) . Assuming an interior solution (OF > 0) , the producer's off-farm wage will equal the marginal value of farm labor:

(10)

The decision to work off-farm can thus be summarized through the following participation rule:

ULlUG = P' * f F ( K , H ) = W'(H, M)'

Eq. 11 says that the producer will work off the farm (D = 1) if the wage rate is greater than the marginal value of farm labor, assuming no off-farm work and evaluated at the point of optimal allocation of time between farm work and leisure. The binary decision rule is thus a function of all the exogenous variables in the model Z since the optimal off-farm work hours, OF*, is determined jointly with farm labor allocation, F*.

These conditions describing the off-farm labor decisions by an agricultural producer are illustrated in Figure 1, which is adapted from Gronau (1977). The production function from time spent working on the farm is depicted by the curve TAE,BY. If all available time is spent on farm work, then the household is able to consume OY units of goods. If all time is spent on leisure activities, then the household is still able to consume OC, due to the presence of exogenous income V . The marginal value of farm labor, P ' f F ( K , H ) , is assumed to decline with the hours devoted to farm work. In contrast, the marginal returns to off-farm labor or real wage rate W' (H, M) is assumed to be constant. The amount of goods that can be consumed with off-farm earnings is represented by the line DBEO R . The tangency point between this line and production curve represents the point at which the individual off-farm wage is equal to the marginal value of farm labor. For additional hours of farm work past Fo, the marginal returns to off-farm work are greater than those on the farm (W' (H, M) > P 'fF ( K , H ) ) and the producer will consequently allocate some time to work in the marketplace. The optimal allocation of the remaining time between off-farm work, OF, and leisure, &,


Figure 1. Off-farm labor participation decision by an agriculture producer

is determined through the standard neoclassical labor supply model as the point at which the marginal rate of substitution between consumption and leisure, - U, I U,, is equal to the wage rate. Changes to prices, technology and/or prefer- ences will affect the optimal choice of labor time. For example, if the producer has a higher preference for leisure than originally depicted ( U , (G, L ) versus U, (G, L ) ) , then the optimal allocation of labor would involve spending F I hours on the farm and the rest as leisure time, L , .

To estimate the participation decision rule, a multivariate logit model is speci- fied as follows:

where P

exp

= the conditional probability of working off the farm, given values for

= the base of the natural logarithm, a = a constant, P’ = a vector of regression coefficients and p = a stochastic error term.

the exogenous variables 2,

OFF-FARM LABOR DECISIONS 24 1

Eq. 12 is reformulated as a logit function, which is linear in the unknown coefficients, a, p ' , and error term for estimation purposes (Huffman 1980).

In ~ = a + p ' l n Z + p [(l P , ]

The dependent variable is the natural log of the odds ratio for a producer working off the farm. The estimated coefficients are asymptotically unbiased, efficient and consistent (Maddala 1983).

The study focuses on the factors affecting the participation of farmers in off- farm employment, which is the first step in the model of labor supply developed by Heckman (1979). In an effort to correct for sample selection bias due to zero hours worked by some individuals, Heckman breaks the analysis of labor supply into two steps. The first step models the labour participation decision (Eq. 13) and the second estimates labour supply using information from the limited dependent variable estimation in the first step. Without data on hours worked, such a labour supply equation cannot be estimated.

DATA

The data used in the analysis were obtained from a survey of Ontario swine producers conducted by Rosenberg (1990). The sample of pork producers was selected by choosing randomly from a list of approximately 12,000 swine operators who marketed weaners, sows, boars and market hogs with the Ontario Pork Producers Marketing Board (OPPMB) in 1988. This is the most comprehensive list of pork producers available, since all swine products except weaners have to be marketed through the OPPMB. A mail survey with 58 questions on farm practices, demographics and farm structure was sent out in mid-September 1989 to 1,920 swine operators. A total of 1,145 producers returned the survey for a response rate of 60%; 120 of these were discarded because the farmers were out of business or did not want to participate.

The decision to work off the farm depends on four categories of independent variables: family characteristics, F, personal human capital characteristics, H, farm characteristics, K , and local labor market conditions, M . The number of children (Sumner 1982) or the presence of preschool age children (Lass et a1 1989) are farm household characteristics that have been found to have a negative impact on the probability of working off the farm. Unfortunately, this information was not collected in the survey but data were obtained on the existence of income other than that earned from the farm or from off-farm employment. Income from sources such as stocks and bonds is hypothesized to reduce the marginal utility of the operator's earnings and will decrease the probability of off-farm employment, assuming leisure is a normal good. A binary variable denoted as OTHINC is eaual to one if the household receives unearned income, and zero otherwise.


The most common human capital variables, H , used in previous empirical studies have been age and education. Age is a proxy for experience, which is assumed to increase both the farm and off-farm labor productivity of the operator. The anticipated effect is thus ambiguous, but prior empirical work has often found a life-cycle effect by including linear and quadratic age terms. In this study, age is measured by categorical variables, as collected in the survey, depending upon the operator age: 36 to 45 years (AGE40), 46 to 55 years (AGESO), and over 56 years (AGE60). Operators under 35 years of age (AGE30) are used as a basis of comparison for the binary age variables.

Education has a similar ambiguous effect on off-farm employment as age in that it is hypothesized to increase both farm labor productivity and the off- farm wage rate. However, previous studies have often found that an increase in the level of education increases the probability of working off the farm (Sumner 1982; Lass et al 1989; Gunter and McNamara 1990). As with age, education is measured by categorical variables according to the highest level of education attained: elementary school (EDELEM), some high school (EDSHS), completed high school (EDCHS), some college (EDSCLG), and at least a college diploma or university degree (EDUD). Operators with a maximum of an elementary school education, EDELM, are used as a basis of comparison.

Specific human capital variables that are expected to increase the marginal productivity value of labor on the farm relative to off the farm are the number of farm-related educational meetings attended, MEEI1vG, and the number of years the operator has been in farming, FARMEXP. The survey does not contain information on human capital variables specific for non-farm work, such as vocational training used previously by Sumner (1982) and Gould and Saupe (1989).

Farm characteristics, K , will influence the value of on-farm labor productivity. Previous studies note the impact of different farming systems on the decision to work off the farm (Sumner 1982; Lass et al 1989; Gould and Saupe 1989). The reason for such a specification is that farming systems that demand extensive labor requirements will be less likely to have operators involved in off-farm employment. The hypothesis can be extended to a single-commodity sector such as swine farms where farrow to finish, FF, and weaner operations, WO, involve more time than finish farms, FO. The three types of swine operation are modeled using binary variables, with weaner operators serving as the basis for comparison.

Another farm characteristic affecting farm labor productivity is the ownership arrangement. Rosen (1978) argues that a partnership or corporate form of ownership permit labor specialization, thereby increasing farm labor specialization. Simpson and Kapitany's (1983) empirical results support this hypothesis but Lass et a1 (1988) find an inverse relationship. They suggest the result may be due to the operators being primarily investors in the farm, which is secondary to their primary careers. A binary variable, CORP, equal to one if the farm ownership arrangement is a partnership or corporation, is constructed to capture these possible effects.


The target income model of Simpson and Kapitany (1983) is based on the assumption that the high financial obligations associated with agriculture force operators to work off the farm. Therefore, an increase in the value of the farm, CAPITAL, will imply a larger target income that may be reached only through the aid of off-farm earnings. In contrast, the neoclassical labor supply model assumes that an increase in the capital stock increases farm labor productivity and thus reduces the probability of off-farm employment. The debt to asset ratio, DAR, is also calculated as a measure of the producer’s financial obligations.

In the optimization problem facing the producer, the individual utility function is maximized through the allocation of labor to farm and off-farm activities and through the choice of variable inputs, X, in the production of farm output. Thus, the level of farm input use and production are determined simultaneously with labor decisions. Farm output can consequently not be included in the off- farm participation model unless it is estimated and the predicted values incorpo- rated. While recognizing the simultaneity of the decision to work off-farm and the allocation of farm inputs, some of the farm inputs can assumed to be fixed for farms in a single cross-section (Sumner 1982; Gould and Saupe 1989). The fixed factors used in this study are the number of tillable acres, ACRES, and barn size, BARN, which is proxied by the number of weaners and hogs marketed.

Local labor market characteristics, M, influence the available off-farm employment opportunities and the wage rate. The number of opportunities is proxied by the population density of the county, POPDEN, in which the farm is located, which is calculated using population and area figures (OMTEIA 1991). The county unemployment rate, UE, is also used to measure off-farm job possi- bilities and is hypothesized to have an inverse effect on the probability of working off the farm. The rate used is the one determined for the region during the month the survey was administered (Statistics Canada 1989).

EMPIRICAL RESULTS

The summary statistics of the independent variables used in the estimation are reported separately in Table 1 for those hog producers who did not work off the farm (OFFWK = 0) and for those who did (OFFWK = 1). In addition, a separate column is included with the value of the test statistic used to test the hypothesis of equal means for each independent variable between the two types of hog producers.

There was no significant difference between those not working and those working off the farm in the proportion of producers receiving income from sources other than that earned on the farm or from off-farm employment. Approximately 13 % of the sampled producers received income from sources such as stocks and bonds.

In contrast to the single variable used to capture differences in family characteristics, there were significant differences between producers without and with off-farm emDlovment in the average levels of the variables used to measure human

244

Table 1. Summary statistics for variables in off-farm employment model"

Variable (OFFWK = 0) (OFFWK = 1) Z-test

CANADIAN JOURNAL OF AGRICULTURAL ECONOMICS

Not working off-farm Working off-farm

Family characteristics (F) Other off-farm income (OTHZNQ

Human capital characteristics ( H ) Age

(yes = 1)

less than 35 years (AGE30) (yes= 1) between 35-45 years (AGE40) (yes = 1) between 46-55 years (AGESO) (yes= 1) greater than 56 years (AGE60) (yes= 1)

elementary school or less (EDELEM) (yes= I ) some high school (EDSHS) (yes= I ) completed high school (EDCHS) (yes = 1) some college training (EDSCLG) (yes= I ) college or university degree (EDUD) (yes= 1)

Farm meetings attended ( M E E W G ) (no.)

Farm experience (FARMEXP) (years)

Farm characteristics ( K ) Farm type:

weaner (WO) (yes= 1) farrow to finish (FF) (yes= 1 ) finish (FO) (yes= 1)

Farm ownership - partnership or corporation (CORP) (yes= 1)

Farm value (CAPITAL) ($OOO) Debt to asset ratio (DAR) Tillable area (ACRES) (no.) Barn size (BARN) (no.)

Education:

0.125 (0.332)

0.273 (0.445)

0.219 (0.414)

0.230 (0.421)

0.278 (0.448)

0.379 (0.486)

0.238 (0.426)

0.160 (0.367)

0.050 (0.219)

0.171 (0.377)

3.32 (4.01)

24.36 (13.89)

0.13 I (0.338) 0.613 (0.487) 0.257 (0.437) 0.444 (0.497)

592.23 (634.05) 0.264 (0.285)

221.72 (191.39)

0.134 (0.342) -0.35

0.298 (0.458) -0.73

0.347 (0.477) -3.76*

0.241 (0.429) -0.36

0.1 13 (0.317) 5.66*

0.172 (0.378) 6.31*

0.257 (0.438) -0.58

0.241 (0.428) -2.64*

0.078 (0.269) - 1.46

0.251 (0.434) -2.56*

3.31 (4.19) 0.07

18.59 (11.44) 5.94*

0.213 (0.410) -2.85*

0.329 (0.471) -2.09* 0.458 (0.499) 4.1 I *

0.404 (0.491) I .05

414.19 (664.33) 3.59*

136.14 (139.06) 6.79* 0.330 (0.304) -2.93*

1024.21 (1264.61) 626.60 (638.08) 5.13* Local labour market characteristics (M) County population density 0.793 (1.297) 0.698 (1.230) 0.97

County unemployment rate (UE) 4.54 (1.68) 4.43 (1.63) 0.85 Number of observations 340 274 "Standard deviations are reported in parentheses. *Significant difference between population means at the 1 % significance level.

(POPDEN) (no/ha2)


capital characteristics. The share of farmers in the 36-to-45-years age category was significantly higher for producers with off-farm jobs, while the share of farmers older than 56 years was significantly higher for those working only on the farm. Given the insignificant difference in the proportions of farmers in the two remaining age groupings, farmers working off the farm were therefore younger on average. These producers also tended to be better educated. Approx- imately 17% of the sampled hog producers working off-farm had attained a maximum of an elementary school education, which was significantly less than the 38% for producers working exclusively on the farm. Similar results were found at the other end of the training scale, where 25 % of those working off-farm had at least a college degree, which was significantly greater than the 17% for those not working off-farm. While there was no difference between the two groups in the number of farm meetings attended, pork producers allocating all work time to the farm had more years of agricultural management experience.

There were significant differences in the farm characteristics associated with the two types of producers. Hog farmers with off-farm employment were more likely to be either weaner or finish operators than producers with no off-farm jobs. Farm value was higher for the latter group but relative debt levels were less, as indicated by the significantly higher average debt to asset ratio for those working off-farm (0.33) versus those who do not (0.26). Average levels of the fixed factors of tillable acres and barn size were significantly higher for producers working only on the farm. There was no difference between the two types of producers in the proportion who had an individual versus a partnership or corporate ownership arrangement.

There was no significant difference found between the average levels of the county population density or county unemployment rate, which were used to measure local labor market characteristics.

Results from the logit model are presented in Table 2 for all hog farmers together and individually for the three types of hog operations: weaner, farrow to finish, and finish. The residual chi-square score statistic and associated P value imply in each case that the hypothesis of no relationship between the dependent and independent variables is soundly rejected. The adjusted pseudo R values are reasonable, given the cross-sectional nature of the data, with the exception of the equation estimated for weaner operators. The models correctly predict 70% of the actual outcomes on average across the four equations. The C statistics, which can range from 0.5 (no apparent discriminatory power) to 1 .O (perfect discriminatory power), for each of the four estimated relationships, are also accept- able, lending further support to the explanatory power of the model (Atkinson 1980).

Existence of income from sources such as stocks and bonds was the only variable available included to measure the impact of family characteristics. It was hypothesized to reduce the probability of off-farm employment by shifting up


Table 2. Parameter estimates for logit model of off-farm labor participationa Farm type Farrow to

Intercept Explanatory variable All Weaner finish Finish

-0.105 -0.717 -1.122 0.524 (0.048) (0.398) (3.459)*

Family characteristics (F) OTHINC Human capital characteristies (H) Age:

AGE 40

AGE 50

AGE 60 Education:

EDSHS

EDCHS

EDSCLG

EDUD

MEETNG

FARMEXP

Farm Characteristics ( K ) Farm type:

FF FO

CORP

CAPITAL

DAR

ACRES

BARN

Labor market characteristics (M) POPDEN

LIE

Model statistics Chi-square (p value)

R= % OFFWK = 0 correctly predicted % OFFWK = 1 correctly predicted ,-

0.194 (0.447)

1.073 ( I 1.235)*

0.986 (8.823)* 0.055 (0.016)

1.144 ( 17.99 I)*

I .426 (23.494)*

1.516 ( 12.408) *

1.506 (23.121)*

0.022 (0.739)

-0.026 (4.545)*

(9.548)*

(0.182) 0.009

(0.002) -0.0002 (0.019) 0.855

(4.969) * -0.0038 (22.444)* -0.oO08 (22.703)*

0. I I9 (2.487) 0.045

(0.569)

-0.835

-0.127

139.25

58.98 73.2 64 h

(0.000l)

0.434 (0.234)

0.451 (0.316) 1.209

( 1 517) -0.229 (0.039)

2.320 (8.369)* 1.266

(2.509)* I .848

(2.527)* 2.985

(8.353)

(0.262)

(0.492)

-0.046

-0.023

-0.063 (0 .o 12) 0.0009 (0.961) 3. I72

(6.603)

(3.461)

(3.332)*

0.283 (3.187)*

(0.145)

37.12 (0.0032) 7.07

64.4

-0.0053

-0.0007

-0.064

0.697 (2.662) *

0.942 (6.151)" 0.741

(2.691)" -0.764

( I ,349)

0.862 (4.748)*

I .880 (22.174)*

1.508 (7.199)* 1.305

(8.556)* 0.006

(0.028) -0.001 (0.009)

0. I87 (0.445)

-0.0004 (1.445)

(2.876)* -0.0019 (2.695) * - 0 .oo 12 (19.081)*

0.159 (0.567) 0.029 (0.108)

66.14 (O.oO01) 35.66 80.7 52.5

-0.910

0.19

(0.341) -0.413 (0.588)

1.646 (8.627)* 1.407

(4.251)* I .097

( I ,498)

0.947 (3.517)* 0.553 (0.832) 1.739

(2.753)* I .302

(3.782)* 1.302

(3.782)* -0.056 (4.393)*

-0.048 (0.015) 0.0005

( I .588) 0.501

(0.395) -0.0062 ( 16.684)* -0.0004 (2.054)

-0. I65 ( I ,106) 0.02 1 (0.029)

56.58 (0.0001) 33.48 64.8

- 68.5 73.5 L 0.79 0.87 -. ~ 0.83

"r-statistics reported in parantheses. *Coefficient significant at 0.10 level


the earnings function from time spent working on the farm (Figure 1). However, no significant effect from exogenous income on off-farm labor decisions was found across any of the estimated relationships.

Human capital variables of age and education have an a priori ambiguous effect on the probability of off-farm employment, given their impact on both farm labor productivity and market wage rate. However, the results obtained here are consistent with most previous studies. Participation in off-farm work was found to increase with age and then to decline, as suggested by the life cycle hypothesis. Probability of working off-farm was found to be maximized at 43 years by Sumner (1982) and at 48 years by Lass et al (1988), which fall in the age categories with the most significant positive effect in this study. As with experience, increases in education appear to have a larger effect on market earnings than the marginal productivity of farm labor. Table 3 indicates that the probability of off-farm employment increases significantly if the maximum level of education is increased from elementary to at least some secondary school. The probability generally continues to increase with further levels of educational training beyond this point but at much smaller increments.

In contrast to age and education, the number of farm educational meetings attended and years of farm experience are human capital variables affecting only the marginal productivity of farm labor and not the wage rate. While no effect could be found for the variable MEEKVG, farm experience was found to have the hypothesized inverse relationship with the probability of off-farm employment. While the effect was minimal for farrow to finish producers, years of farm experience had a large impact for finish operators.

Farm characteristics also influence the marginal value of farm labor. As was assumed given the generally more extensive labor requirements for this type of hog operation, farrow to finish producers were less likely to work off-farm than weaner operators. The binary variable for finish farmers was not significantly different from zero. Neither was the binary variable indicating ownership arrange- ments nor the value of farm assets. Both variables could have been either positive or negative depending on the theory put forth. A partnership or corporation may increase farm productivity through labor specialization, or it may be an indicator that the farm is a secondary career alternative. Similarly, neoclassical theory suggests that increases in the farm asset base increase the marginal productivity of farm labor, while the target income model of off-farm work choice by Simpson and Kapitany (1983) suggests that such increases in farm asset value increase financial obligations, which may be met only through off-farm earnings, given no subsequent increase in farm income.

While neither theory related to farm asset value could be empirically supported, the importance of financial obligations in the off-farm employment decision was indicated by the significant positive coefficient on the debt to asset ratio. Table 3 indicates that across all hog farms the probability of working off-farm


Table 3. Effect of selected independent variables on probability of off-farm employment" ~~

Farm type ____ ~

Farrow to Independent variable All Weaner finish Finish

Age 35 years or under 36 to 45 years 46 to 55 years 56 years or older

elementary school some high school completed high school some college college or higher

Farming experience 5 years 10 years 20 years 40 years

Debt to asset ratio 0.00 0.25 0.50 0.75 1 .oo

Tillable area 50 acres 100 acres 250 acres 500 acres

Population density 0. l /ha2 0.5iha2 1 .0/ha2 2.0/ha2

Education

.29

.54

.52

.30

.21

.45

.52

.55

.54

.52

.49

.42

.30

.35

.40

.45

.51

.56

.53

.49

.35

.17

.39

.40

.42

.44

.60

.71

.84

.55

.31

.82

.61

.74

.90

.75

.73

.68

.58

.34

.53

.72

.85

.92

.77

.73

.54

.23

,63 .66 .69 .75

.22

.42

.37

. l l

.I3

.26

.49

.40

.35

.28

.28

.28

.27

.22

.27

.31

.36

.42

.33

.3 1

.25

.17

.26 -27 .20 .32

.29

.68

.62

. 55

.35

.58

.48

.75

.66

.77

.71

.58

.31

.so

.53

.56

.59

.62

.74

.68

.45

.15

.56

.54

.52

.48 ~

"Probabilities evaluation at the means of independent variables in each subpopulation.

is less than 40% if the operation is very solvent (DAR < 0.25) and increases to over 50% if the debt to equity ratio is greater than 0.75. However, the impact of DAR on off-farm employment decisions is much greater for weaner producers


compared with the other two hog farm types. The result may be explained by the transitional nature of weaner producers. Many wish to finish more of their own pigs but are constrained by barn space and available capital. In order to generate funds for expansion, such an undertaking may require additional earnings that cannot be raised on the farm. Thus, they are more likely to work off-farm than the other two farm types, and the probability increases significantly if they are already facing credit constraints.

An increase in the fixed factors of tillable area and barn size as measured by the number of pigs marketed is found to have the hypothesized inverse effect on the probability of working off-farm. Given a wage rate, the marginal productivity of farm labor is greater on a bigger farm than on a smaller one, implying the larger operator is less likely to work off-farm. The results are also consistent with Sumner’s (1982) suggestion that specialization increases off-farm work. Since all farmers in the survey were hog producers, an increase in tillable area may imply a diversification into cash cropping, which thereby decreases the probability of off-farm employment. This is especially true for finish producers (Table 3 ) .

The final variables included in the model were local labour market characteristics. While the county unemployment rate was found to have no significant impact, population density for the county had the hypothesized positive impact on the chances of working off-farm. The result suggests that it is the absolute rather than the relative number of job opportunities that has the largest effect on an operator’s off-farm employment decisions. However, Table 3 indicates that the effect of labor market characteristics is much smaller than that from the other types of explanatory variables.

CONCLUSION This paper examines the off-farm employment decisions of Ontario swine producers using household production theory. It is determined that human capital and farm characteristics have the largest impact on off-farm labor participation. Increases in education are found to have a larger effect on market earnings than on the marginal value productivity of farm labor, thereby increasing the probability of off-farm employment. The chances are also found to increase with an increase in relative financial obligations, as measured by the debt to equity ratio. Years of farming experience and farm size have a negative impact on the probability of working off-farm due to their positive relationship with marginal farm labor productivity.

Several inferences and policy implications can be drawn from the study. The first is that the trend to a bimodal farm size distribution will likely continue in the Ontario hog industry, since it is the middle age cohort of producers who are most likely to work off-farm, while the young beginning farmer cohort will operate large commercial operations in each forthcoming generation of producers. The trend is also supported by the finding that an increase in relative debt servicing


requirements increases the probability of off-farm employment. New entrants into agriculture will often choose hog production given its smaller capital invest- ment requirements and fewer institutional barriers relative to other major livestock sectors. However, the costs are still large and the level of financial obligations may force the need for off-farm earnings and in the process limit the size of the hog operation that can be managed. The existence of such opportunities will help weather fluctuations in farm income, but the results of this study indicate that economic development policies designed to increase family farm income will have little impact on the off-farm employment decisions of Ontario hog producers. The major influences on this decision are human capital and farm characteristics.

The final policy implication concerns targeting extension efforts. The common approach of educational meetings would not appear to be affected given the insignificant difference in attendance of such meetings by those working off-farm and those who are not. However, the content may have to differ, since off-farm workers tend to be better-educated, middle-aged individuals running a smaller operation on average than the larger, commercialized farms of producers working only on the farm.

NOTE 'Since the price of the consumption goods is used as the numeraire, the marginal rate of substitution is in the same units as the other wages, dollars per unit of labor (Sumner 1982).

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OFF-FARM LABOR DECISIONS 25 1

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