Motivation

The Chilean small farmers face a complex situation as a result of a more intensive open trade process in recent years. Additionally, it is clear that the living conditions of the rural inhabitants depend more strongly on off-farm earnings. In this context, public policy has many challenges, on the one hand, to enhance a higher level of productivity at the farm level and, on the other, to provide development instruments in post non-farm activities in rural areas. This challenge is really hard, especially when rural development is not a national priority.

It is a fact that farm households across the developing world earn an increasing share of their income from non-farm sources. Barrett et al. (2001) analyzed the non-farm income diversification and household livelihood strategies in rural Africa, Reardon et al. (2001) studied the situation in Latin America, Chaplin et al. (2003) provided information about non-agricultural farm diversification in Central European countries, Fernandez-Cornejo et al.

(2007) focused on the United States. In order to improve public policy Readon et al. (2006) remarked on the necessity of understanding the nature and patterns of household income diversification, and distinguished the factors that drive households into non-farm activity.

Berdegue et al. (2001) is the known unique study that deals with non-farm incomes for the Chilean case. They analyzed the evolution of rural non-farm income and employment in Chile from 1990 to 1996 and showed that during this period rural non-farm income increased 18%

and accounted for 41% of the rural income while rural non-farm employment increased 10%,

78

The main question is whether the technical efficiency of small farmers is affected by the decisions of participation in off-farm activities or not, and if so, what kind of relationship exist?. This issue has not been deeply studied in the literature although its importance is clearly fundamental. The few studies are not conclusive and are based on different methodologies which make a difficult comparative analysis.

Based on data at the farm level, we formulate and estimate technical efficiency for small non-specialized Chilean farmers by using stochastic frontier analysis, considering heteroscedasticity in the inefficiency estimated and endogeneity in the decisions of participation in off-farm activities. We focus on farmers that have some grade of diversification in their agricultural production mix because we are also interested in studying how this diversification affects the levels of efficiency. In this way we could get some insights about how the agricultural production structure affects the scores of technical efficiency.

From an empirical point of view, the study provides four contributions. First, this study will be the first one in Chile that deals with estimations of technical efficiency and technological parameters of small farmers by using a country wide sample for non-specialized producers.

Second, the production technology of farmers is modeled by taking into account differences in partial production elasticities among different sectors. Few studies have dealt with multi product technologies (Bravo-Ureta et al., 2007) due to the difficulties in its modeling. The literature reviewed has used dummy variables by modeling different intercepts among sectors (O’Neill and Matthews, 2001, Rezitis et al., 2002, and Dong, 1997) without taking into account differences in the partial production elasticities which clearly lose valuable information of the underlying technologies.

Third, the variance of the inefficiency term is modeled instead of its mean. The few studies that have followed this method are basically focused on agriculture of higher size. Thus, this research contributes by analyzing to small farmers and incorporating a wide range of variables to explain the variance of the inefficiency term.

Fourth, using a framework of stochastic frontier analysis, the statistical relation between technical efficiency and participation in off-farm labor is studied by considering the potential endogeneity of the participation.

6.2 Model specification and data analysis 6.2.1 Model specification

Based on the on-farm income of each activity a Herfindahl index was built and the grade of specialization of each farmer was identified, taking those farmers with an index smaller than 1. Thus, we got a final sample composed of 384 non-specialized producers (see Chapter IV).

The sample was divided into three subgroups depending on the grade of orientation in the production. Dummy variables were used to do that. DLIVE takes the value 1 if livestock income share is equal or larger than 70%, DCROP takes the value 1 if crop income share is equal or larger than 70%, and DMIX takes the value 1 if there is not any production activity with an income share greater than 70%. In other words, DMIX measures farmers that are not specialized at all.

Similar to Chapter V, the technology is represented by a Translog production function as follows

where β, α, ω, λ, φ and Φ are unknown parameters, v_i is a pure random term (white noise) and ui is a systematic, nonnegative error term accounting for efficiency. The dependent variable is the weighted on-farm total income using as weights the shares of each activity (crop, livestock, fruit and vegetables), and log y is the decimal logarithm of this variable. As was defined in Chapter V, Log x is the decimal logarithm of four inputs; used land (A), working capital (WC), the market value of livestock (AV), and the input estimated family labor force (T). We use three additional variables to take into account differences in the quality of soil (PIA), localization of the farms (DL2, DL3, DL4, and DL5), and potential bias of the parameters estimated in the production function (DAV). Additionally, two new variables are added; DCROP and DLIVE which are dummy variables that capture the differences in the production orientations among farmers, incorporating them as intercepts and slopes in the production function. Thus, the parameter β0 represents the mean on-farm income level of a

μ

80

Most of the signs expected were discussed in Chapter V; however, the signs expected of the new variables (DCROP and DLIVE) both in the intercepts and in the slopes (DCROP*log x and DLIVE*log x) are indeterminate a priori.

The inefficiency model is discussed in Chapter V, and is formulated as follows

where φj are parameters to be estimated that measure the influence of variables in z on efficiency, and ξi are independent and identically distributed (i.i.d.) normal random variables with a mean of zero and constant variance.

The vector z represents factors that influence efficiency which includes several categories of variables. Three variables account for socioeconomic characteristics of the farm household (AGE, EDU, and FS), One variable related to tenure (POA), one variable measures access to markets (ACC), one variable that represents public policy (DINDAP), on variable associated with credit market (CRED), and ten variables that capture management decisions (A, WC, AV, T, DMANAG, DEX, SHA, DCROP, DLIVE and DOFF). The dummy variable DOFF measures the participation of farm in off-farm activities, taking the value 1 if the household participates. Notice that the intercept in the inefficiency model is the mean variance of a diversified farmer (DMIX).

According to Section 3.2.5, Chapter III, the sign of the relation between participation in off-farm activities and technical efficiency is ambiguous, so we formulate no a priori expectations for this variable⁴⁰. On the other hand, DCROP and DLIVE should have a negative effect on efficiency if efficiency increases with a higher on-farm production diversification.

40 Participating in off-farm activities can either increase efficiency by means of relieving financial restrictions or reduce efficiency as a result of a lower effort of farmers on farms.

ξ φ σ

σ_μ = +

∑

= j

j jz

16

1

log 0

6.2.2 Data analysis

The descriptive statistics of the sample are given in Tables 27, 28 and 29. According to our definition of farmer, the mixed, crop-oriented and livestock-oriented producers account for 42%, 29% and 27% of the sample, respectively. They are concentrated in macro zone 4 and 5 with more than 80% of the sample. Moreover, on average, 40% of farmers get credit of short term, basically financed by INDAP (72%), Banco Estado (14%), and Oriencoop (3%). For those farmers that can get credit, it accounts for between 28% and 41% of their financing.

Table 27

Descriptive statistics of non-specialized producers (sample: 384 observations)

82

A 55% of the sample participates in off-farm activities and, on average, the off-farm income represents 33% of their income sources. At the type of producer level, it is possible to see that for those farmers that participate in off-farm activities, the group of crop-oriented producers has the lowest participation (27%) and the group of mixed producer has the highest one (62%) Additionally, it is remarkable the fact that when farmers are unable to get formal credit, the informal credit market does not provide the necessary funds and the selling of fixed asset and the owned resources become very important.

Table 28

Financing sources of working capital with and without formal credit by sub group

Status Sub sector Formal Informal Selling of

Fixed Assets Own Resources

(1) Includes seeds, savings, rented land, profits from previous season and selling of animals

(2) Any item that is not included in the others, for example, pensions and income from off-farm activities Source: Own calculations using survey data

Table 29

Share of the income sources with and without participation in off-farm activities by sub group

Sub Group On-farm Off-farm Other Income (1) Total

(1) It basically includes subsidies and pensions.

Source: Own calculations using survey data