IntroductionTop

Since the 1970s Chile has been open to global trade (Fleming & Abler, 2013). As a consequence of successful international trade, an ambitious agricultural policy started in 2006 to position the country among the world’s top 10 agricultural exporter countries by 2015 (Campos & Polit, 2011). This ‘open to global trade’ policy implied new challenges to achieve competitiveness of traditional agricultural areas in order to survive, such as gaining participation in international trade and adoption of agricultural innovations (David et al., 2000; Echeverria et al., 2009; Moreira & Bravo-Ureta, 2010).

The exit of smaller and less productive farms from agriculture is beneficial for the agricultural sector regarding its efforts to become competitive in world markets, and also for allocating resources between agriculture and other sectors (Breustedt & Glauben, 2007). However, it has an effect on equity within agriculture, productivity and efficiency of farming, as well as on the welfare of rural communities. This effect can be translated as an increase of large and high tech farms, a decline in farm employment, a decrease of family farms and the out-migration of farm and non-farm residents. The ultimate result is a declining number of farmers and an aging rural population (David et al., 2000; Chang et al., 2011; Raggi et al., 2013). In Chile the rural population has decreased from 40% during the 1980s to 8% in the last decade (Oyarzún & Miranda, 2011). This is mainly rooted in the fact that younger and more educated people have migrated away from agriculture (Fawaz & Vallejos, 2011), as their better education makes non-rural employment opportunities more accessible (Ramírez et al., 2001).

Researchers and policy makers have developed long-term strategies to mitigate the negative consequences of the exit from agriculture and to sustain or rejuvenate the rural population. These strategies have considered long-term financial assistance for farmers to increase production efficiency, such as commodity subsidies, price controls, diverse forms of market protection, government-funded R&D and education (Chang et al., 2011). In Chile, the Agricultural Development Institute – INDAP1 is the government organisation mandated to assist small farmers2 , in order to reduce the economic gap between rural and urban areas to make them competitive at global trade (MINAGRI, 1990; INDAP, 2013a). To counteract the exit from agriculture, INDAP provides technical and financial support to fostering entrepreneurship among peasant families (as a first step) and then promotes them as successful firms able to compete in the global trade (as a second stage). The INDAP investment in consolidating and expanding existing farm businesses, i.e. farmers in the second stage (15% of INDAP users), has increased 65% in the last 5 years. However, the number of INDAP users has been increased by only 3% (INDAP, 2013b), which indicates that current INDAP programs have not been successful in achieving their goals.

To increase the likelihood to stay in farming, a good understanding of the factors underlying the exit process and economic development in rural areas ought to be achieved (Henning et al., 2013). Therefore, a more accurate understanding of the stay-exit mechanism is essential for the design of well targeted and efficient policies that enhance the future structure of farming, land management, population and employment dynamics in rural areas (Raggi et al., 2013).

A number of studies on Chilean agriculture have been either focused on the economic and technical efficiency of farms (Smith et al., 2002; Lerdon et al., 2010; Moreira & Bravo-Ureta, 2010) or the characteristics of the farmer and labour force (Vera & Moreira, 2009; Carmona et al., 2010). However, there are no studies that examined the factors driving the exit intention of farmers in Chile, nor in Latin America.

This paper aims to fill this knowledge gap by investigating the main factors driving the stay-exit intention of small livestock farmers in three regions of southern Chile. This study provides information for decision-makers that is useful to design well-targeted and efficient rural policies.

Material and methodsTop

Theoretical foundation for the study

Stay-exit decisions of farmers have been studied in the past (Boehlje, 1992; Rosenbaum & Lamort, 1992; Gale, 1994; Karakaya, 2000). There are various theories that partially explain these intentions. Particularly, three theories have been developed: efficiency theory, exit barriers theory and the life-cycle theory (Boehlje, 1992; Pushkarskaya & Vedenov, 2009). We started the study by merging these three theories into a holistic theoretical framework, aimed at obtaining a sound scientific foundation for questionnaire development and subsequent analysis. This theoretical framework is presented in Fig 1.

Figure 1. Theories and their main driving factors involved in stay-exit intention problem.

Efficiency theory is the main theoretical framework for explaining the exit decision (Boehlje, 1992; Pushkarskaya & Vedenov, 2009). It compares the expected utility of staying in farming, E(U)stay, with the expected utility of exiting farming, E(U)exit. The E(U)stay is a function of financial factors, such as farm income and non-farm income, as well as of non-financial ones, such the social and network status. The E(U)exit is mainly determined by labour income (which depends on job availability and skills), and by factors such as living expenses and availability of services. This theory assumes a rational decision maker that has access to all the required information to make a decision; if that is the case then the alternative with the highest expected utility E(U) is preferred. However, assumptions are never fulfilled. Therefore, two complementary theories are used: exit barriers theory and life-cycle theory (Karakaya, 2000; Pushkarskaya & Vedenov, 2009). Exit barriers theory focuses on direct or indirect obstacles, which limit the rational behaviour assumed by the efficiency theory. Direct barriers are obstacles on their own, such as the sunk cost of previous investments, while indirect barriers affect the E(U) by reducing the capability of acquiring new skills. This theory explains why enterprises continue operating even while releasing a very low profit or a loss (Chang et al., 2011). Life-cycle theory focuses on specific human capital (Gale, 1994). This theory assumes that farmers make input, production, and investment plans that are optimal given their biological life cycle (Boehlje, 1992). In this theory, factors that influence the stay-exit decision include farmer’s age, family size, own knowledge, and risk attitude.

The developed framework allowed to categorise all the variables in a logical order and certain hierarchical way, providing a consistent theoretical foundation for selecting the variables3 we have used in this study.

Questionnaire development

We used the holistic framework shown in Fig. 1 to develop a questionnaire focused on factors that might influence the stay-exit intention of the farmers. It included both qualitative and quantitative close-ended questions4 on characteristics of the farmer and his family, farm production, satisfaction level, and expectations of the farmer. The variables we analysed in this study and their expected effects on the stay-exit intention are summarised in Table 1.

The variables we identified as having potential impact on stay-exit intention and their expected effect on this intention are the following: from the Efficiency theory, we used INCENO, HA, MIXFARM, LIVESMIXFARM, and DIVERSIFICATION to capture the effect of farm income (Bragg & Dalton, 2004; Foltz, 2004; Pushkarskaya & Vedenov, 2009). We expected these variables to have a positive impact on the intention to stay. We used OFFINCREL to capture the effect of off-farm activities (Bragg & Dalton, 2004; Pushkarskaya & Vedenov, 2009; Zhan et al., 2012). We expected this variable to have either a positive or negative effect on the intention to stay. We used ASSOCIATION, NETWORK, RECOGNIZE, and INDEPENDENCY to capture the effect of non-financial variables (Gasson et al., 1988; Fairweather & Keating, 1994). We expected these four variables to have a positive association with the intention to stay. From the Exit barrier theory, we used BUILDINGS and LEACOST to capture the effect of sunk costs (Rosenbaum & Lamort, 1992; Karakaya, 2000; Goetz & Debertin, 2001; Foltz, 2004). We expected these variables to have a positive association with the intention to stay. We also used the variable DISTANCE (Goetz & Debertin, 2001) and we expected this variable to have either a positive or negative association with the intention to stay. From the Life-cycle theory, we used AGE, AGESQ, FEMALE, MARRIED, SCHOOLING, AGRIEDUC, and NONAGRIEDUC to capture the effect of characteristics of the farmer (Boehlje, 1992; Bragg & Dalton, 2004; Baylina & Salamaña, 2006; Pushkarskaya & Vedenov, 2009; Charatsari et al., 2013). We expected AGE, AGESQ, and FEMALE to have a positive effect on the intention to stay; while we expected the other four variables to have either a positive or negative effect on the intention to stay. We used FAMSIZE, FAMLAB, and SUCCESSOR to capture the effect of the characteristics of family of the farmer (Zollinger & Krannich, 2002; Glauben et al., 2006; Breustedt & Glauben, 2007; Hennessy & Rehman, 2008). We expected these variables to have a positive association with the intention to stay. We used RETIREAGE and SALEPRICE to capture the effect of farmer willingness to exit farming (Pushkarskaya & Vedenov, 2009). We expected these two variables to have a negative association with the intention to stay. We include SATISFY and LIFEEXP to capture the effect of overall satisfaction and positive expectation, respectively (Hellman, 1997; Zollinger & Krannich, 2002, Kuehne, 2013). We expected these variables to have a positive association with the intention to stay. Finally, we also included the regions to identify the effect of the area.

Study area and sampling population

We conducted this study in three administrative regions in southern Chile: La Araucanía, Los Ríos, and Los Lagos (see Fig. 2).

Figure 2. Map of the study area.

These regions cover 24% of the total land used for livestock and forestry in Chile, but account for 49% of all livestock and forestry farms. Sixty per cent of bovine producers are situated in these regions, who manage 63% of all beef cattle and 91% of all dairy cows in Chile (INE, 2007). Eighty percent of the farms are managed by farmers older than 45 years, and 70% of farmers have no more than 8 years of formal education, i.e. preparatory school (Apey & López, 2011). It is also where 45% of the total number of INDAP users are located (INDAP, 2013b). In these regions INDAP assists above 60% of the small farmers, reaching the 82% of them in the Araucanía region (INDAP, 2015). Farm sizes range from 10 to 200 ha of land with 0.62 to 1.16 AU5/ha. Feed comes either solely from pastures, or in combination with supplementary concentrates (Vera, 2006). The coexistence of small farms (focused on timber logging, livestock breeding, and small-scale cultivation of cereals and potatoes) with larger and more entrepreneurial farms is common in this area (Barrett et al., 2002; Vera, 2006). We chose this area because one of the participating research centers is located here, allowing access to the names and addresses of farmers.

In January 2014, we obtained a database from each INDAP Regional Office, containing the age of the farmer and the size of the farm. We selected the INDAP users that had at least basic information about their farming system, i.e. production level records. From this database 300 farmers were selected using a stratified random sampling method (Neyman, 1934). We used the farm size as a proxy to determine the optimum sample size by region. The questionnaire was applied by the agriculture advisors of the farmers to increase participation and obtain personal information. At the end of the process, 212 questionnaires were returned between January and March 2014 by those farmers who gave their consent to participate in this study. The representativeness of the sample in terms of farm size and age of the farmer is shown in Table 2, where the mean and standard deviation for these two variables are shown for the sample and total population.

There is a small difference in the variance of the farm size for Los Lagos region, which is attributable to the larger size of this region and the fact that larger farms are located in more remote areas. Otherwise the sample is considered to be representative, as no other differences in regional values between the sample and population were found.

The authors acknowledge the fact that there is a possible bias by including only INDAP users in the sample, which could lead to results and conclusions only applicable for them. Nevertheless, this sample was the best alternative considering the focus of the study and the data used.

Statistical modelling

After database development, potential variables of having an impact on stay-exit intention (Fig. 1) were pre-tested for multicollinearity using a correlation matrix. In this way, the number of the variables to be included in the following model was reduced.

We used a binary choice model to identify the impact of the selected variables on the intention to stay of the farmer, considering that his/her final intention has two possible outcomes i.e. stay or exit. This kind of model holds only two values for the unobserved dependent latent variable : 0 or 1 (Verbeek, 2012). In our study, the dependent variable indicates the likelihood that the ith farmer will stay on the farm during the next five years; where a value of 1 indicates the likelihood to stay and a value of 0 indicates the likelihood to exit. Since each farmer has his/her own preference, which is determined by many independent and individual factors, we assumed a standard normal distribution for the error. Therefore, we used a probit model as following (Eq. [1]):

where the explanatory variables are represented by , the coefficients to be explained are represented by , and the random error term is represented by (Verbeek, 2012).

We measured the goodness-of-fit for this model in terms of the significance-of-fit and the proportion of correct predictions between calculated probabilities and observed response frequencies6 (Domencich & McFadden, 1975; Dhrymes, 1986; Hoetker, 2007). Since no single pseudo-R2 covers these points7, we chose McFadden pseudo-R2 and Count R2 to evaluate the goodness-of-fit (Veall & Zimmermann, 1996; Hoetker, 2007; Wooldridge, 2012). The McFadden pseudo-R2 is the most frequently used measure in discrete choice models because it uses the log-likelihood provided by the probit model, and because it is less sensitive to misspecification in the error term8 (Veall & Zimmermann, 1996). The McFadden pseudo-R2 indicates a percent increase in the log-likelihood function ( Eq. [2]):

where LM and L0 are the likelihood of the model with and without regressors, respectively, subject to the constraint that all the regression coefficients except the constant term are zeros. The Count R2 transforms the continuous predicted probabilities into a [0-1] scale, and gives the proportion of correct predictions ( Eq. [3]) (Hoetker, 2007).

We also compared the key characteristics of the exit and stay groups after the probit model estimation. This comparison provides potential insights that can be used to design efficient policies. A t-test was used for metric variables and a Mann-Whitney U test was used for non-metric variables.

ResultsTop

The descriptive statistics of the variables used in this study and the empirical results obtained from the estimation of the probit model are summarised in Table 3. Table 3 shows the descriptive statistics of the variables we use in this study (columns 2 and 3). It also shows the coefficient estimates, the marginal effect, and the p-value for the probit model (columns 4 to 6). Since space is limited, independent variables that are found significant for the probit model are described only.

Table 3 shows that 63% of the farmers indicate they will stay on farming during the next five years (our dependent variable: STAY).

Regarding the efficiency theory, Table 3 shows that in terms of financial variables a 77% of the farmers indicate that their income from farming is enough to cover their expenses (INCENO). It also shows that 28% of these farms produce both crops and livestock (MIXFARM), 17% of them produce livestock as the main product (LIVESMIXFARM). In terms of non-financial variables, almost half of the farmers indicate that they belong to a formal association (ASSOCIATION), while over 60% indicate that farming allows them to make their own decisions (INDEPENDENCY). Table 3 also shows that, with regards to the exit barrier theory, the average distance to the nearest city is 21 km (DISTANCE), and regarding the life-cycle it was found that 21% of the farms are owned by women (FEMALE) while the average family size is 2.5 members (FAMSIZE). Furthermore, it is also shown that about 30% of the farmers indicate to have either a defined retirement age (RETIREAGE) or a defined sale price for the farm (SALEPRICE). The farmers indicating to have a positive expectation of their farming life in the future (LIFEEXP) accounted for 75%.

Our findings for the probit model show that 12 out of the 30 parameters are significant at the 10% critical level (in bold in Table 3). Although the coefficient estimates are presented in Table 3 (column 4), the effect of changes in the explanatory variables should be interpreted considering the marginal effects (column 5); a positive value of the latter means the probability of the stay intention increases with that variable.

We found strongly positive associations (p<0.05) with the stay intention for two variables related with life-cycle theory, i.e. FEMALE and LIFEEXP. However, we found intermediate positive associations (p<0.10) with three variables related with the efficiency theory, i.e., INCENO, MIXFARM, and ASSOCIATION; with one variable related with the exit barrier theory, i.e., DISTANCE, and with one variable related with the life-cycle theory, i.e. AGESQ. Additionally, we also found another weaker positive associations (p<0.20) for OFFINCREL, AGE, and FAMLAB.

Moreover, we found strongly negative associations (p<0.05) with the stay intention for two variables related with the life-cycle theory, i.e. RETIREAGE and SALEPRICE. However, we found intermediate negative associations (p<0.10) with two variables related with the efficiency theory, i.e. LIVESMIXFARM and INDEPENDENCY; and with one variable related with the life-cycle theory, i.e. FAMSIZE. Additionally, we also found other weaker negative associations (p<0.20) for both AGRIEDUC and LAGOS.

The goodness-of-fit measures of the model show a McFadden pseudo-R2 = 0.25, which is within the range considered to be extremely good, i.e. between 0.2 - 0.4 (Louviere et al., 2000)9 . The Count R2 value indicates that the model correctly predicted the intention to stay for 76% of the observations. These values indicate that the explanatory power of the variables in the model is relatively high.

The comparison between groups of farmers likely to stay and those likely to exit is shown in Table 4. Table 4 shows that 15 of the 30 variables are significantly different between these two groups at the 10% critical level, with 9 of the 30 variables significantly different at the 5% critical level. We found strong statistical differences between groups (p<0.05) for two variables related with efficiency theory, i.e. INCENO and DIVERSIFICATION; and for five variables related with life-cycle theory, i.e. AGE, AGESQ, FEMALE, RETIREAGE, and LIFEEXP. We also found strong statistical differences between groups for RIOS and LAGOS. We found intermediate statistical differences between both groups (p<0.10) for two variables related with the efficiency theory, i.e. OFFINCREL and RECOGNIZE; for three variables related with the life-cycle theory, i.e. FAMLAB, SALEPRICE, and SATISFY. We also found intermediate statistical differences between groups for ARAUCANIA.