The Impact of Agricultural Extension Services on Farm Household Efficiency in Ethiopia

Transcription

1 The Impact of Agricultural Extension Services on Farm Household Efficiency in Ethiopia Paul Thangata 1 and Teferi Mequaninte 2 1 Research Fellow, IFPRI, ESARO, PO Box 5689, Addis Ababa, Ethiopia p.thangata@cgiar.org; Tel: / Fax: Research Officer, International Food Policy Research Institute (IFPRI), Eastern and Southern Africa Regional Office (ESARO), t.mequaninte@cgiar.org, PO Box 5689, Addis Ababa, Ethiopia

2 1. Introduction One of the major policy shifts in Ethiopia since 1992 has been the substantial emphasis placed on improving the productivity of smallholder agriculture through increased use of a package of improved agricultural technologies. Smallholder agriculture producers are increasingly able to select economically viable technologies and practices for maximum and efficient production (MoFED, 2010). The Rural Capacity Building Project (RCBP) is one of the many programs designed to enhance the productivity of farmers in Ethiopia. The RCBP is a World Bank/CIDA financed program implemented by the Ministry of Agriculture. The project supports the government s endeavor to improve the access and quality of the agricultural extension system and make the service client oriented to address the demands of farmers and pastoralists. The overarching expected outcomes of the initiative are to expand the choices of adoption of new and usable agricultural technologies, increase agricultural productivity, and thereby bring significant positive income change for farming households. This would be achieved by: (i) modernizing the Agricultural Extension, Technical and Vocational Education and Training (ATVET) colleges by making them more responsive to the changing needs of a demand-driven and market-driven agricultural sector; (ii) building capacity in the agricultural extension system while piloting new initiatives to introduce demand-driven and participatory mechanisms; (iii) a strengthened agricultural research system with improved institutional and human capacity to generate and disseminate client-demanded and marketoriented technologies; (iv) development of agricultural market institutions; and the integration of gender equality, HIV/AIDS and environment issues. Studies of grain farmers have investigated the optimal way of converting inputs into outputs, i.e. raising technical efficiency (Heshmati and Kumbhakar, 1997; Seyoum et al., 1998; Wilson et al., 2001). Seyoum, et al. (1998) investigated the efficiency of maize producers in eastern Ethiopia. Nonetheless, little attention has been given to the impact of agricultural extension services on productivity and efficiency at the farm level. This paper aims to fill this gap. 2

3 As part of the mid-term evaluation of the RCBP, a study was carried out to assess whether RCBP participating smallholders have a greater overall productivity and technical efficiency than farmers outside the project. The objective of this paper is, therefore, to investigate and compare the technical efficiency of project participants and non-participants. Agricultural production is heterogeneous and farms differ in many ways, making it important that this heterogeneity is accounted for in production analysis (Just, 2000; Just and Pope, 2002). Heterogeneity may also be present because of the targeting of farm households by agricultural researchers and extension services. Depending on who is targeted by these public service providers, attitudes towards farming and skills are likely to vary across individual farms. Studies have shown that farmer characteristics such as farmer s age, farming experience, access to extension services, marital status, and education level affect farm productivity (Seyoum, et al., 1998; Pickett, 1991; Uaiene and Arndt, 2009). The frontier production function specifies what output can be achieved, if all decisions were taken according to their best practices (Friebel et al, 2003). In smallholder farming, a farm s technical efficiency is a measure of its ability to produce relative to the smallholder s bestpractice frontier, the maximum output possible from a given set of inputs and production technology (Aigner, Lovell, and Schmidt 1977; Meeusen and van den Broeck 1977). Technical inefficiency on the other hand is the deviation of an individual smallholder farm s production from the best practice frontier. The level of technical efficiency of a particular firm (farm in our case) is based upon deviations of observed output from the efficient production frontier (Greene, 1993). If the actual production point lies on the frontier it is perfectly efficient. If it lies below the frontier then it is technically inefficient. The distance between the actual to the achievable optimum production from given inputs, indicates the level of production inefficiency of the individual firm (Greene, 1993; Friebel et al, 2003). We estimate a stochastic frontier production function to analyze differences in technical efficiency between RCBP participating and non-participating smallholders in Ethiopia. The analytical framework used in this paper follows the Cobb-Douglas production function model of the Stochastic Frontier Analysis (SFA). The SFA assumes the existence of technical inefficiencies of production of farmers involved in producing a particular output. The Cobb- Douglas production function expresses output as a function of inputs which capture the degree to 3

4 which farm households produce below the frontier level of production, i.e. inefficiencies (Kumbhakar and Lowell, 2000). 2. Materials and Methods The data used for our empirical analysis are drawn from the household survey of 1609 rural households in Amhara, Oromiya, SNNP and Benshangul-Gumuz regions of Ethiopia, conducted by IFPRI from December 4, 2009 January 5, The surveys were conducted using structured interviews with multistage stratified sampling technique to collect quantitative individual level information. After eliminating several observations that have missing values on variables such as input use, the final data set includes 678 farmers, of which 351 farmers were RCBP and 327 non-rcbp farmers. The household questionnaire was supported by focus group discussions with selected groups of farmers of about people for both the project and non-project households. Each focus group was made up participants representing the poor, better off, those living near and far from the Farmers Training Centers (FTCs) and old and young people. Each of the groups was later subdivided into two groups, male and female Analytical framework and econometric modeling As in Battese and Coelli (1995), the paper follows a two step estimation model. The first step involves the specification and estimation of the stochastic frontier production function and the prediction of the technical inefficiency effects, under the assumption that these inefficiency effects are identically distributed. The second step involves the specification of a regression model for the predicted technical inefficiency effects. We estimate separate stochastic frontier production functions for RCBP participating and nonparticipating smallholders. The data for project and non-project farmers are combined in such a way that the levels of production in the two groups are differentiated by the use of a dummy variable. The model for farmers within and outside the RCBP project is specified by a stochastic production frontier of the form: (1) 4

5 Where: The dependant variable is the natural logarithm of the total real crop output per household. This single output is an aggregate of multiple crop outputs. The natural log of such an output is presented in Figure 1 and the shape of the histogram is close to the normal distribution..4.3 Density Log real output Figure 1 Natural log real output per farm household Labor and land are transformed using the natural logarithm transformation before they are used in the estimation of farm household coefficients. Both land and labor are the two major inputs in Ethiopia, a characteristic of smallholder agriculture and insignificant capital use. Di represents the project and non-project dummy variable, which has value 1 for farmers in the project group and 0 otherwise. Labor represents number of adults aged 14 and above in farming; land represents farm size in hectares; input cost represents the cost of inputs used in Ethiopian Birr. The parameters to be estimated (the ) represent the elasticity of output with respect to each input i, that is, the percentage change in output that results from a 1 percent increase in input i; the are assumed to be independent and identically distributed random errors with - distribution; and the s are non-negative random variables, called technical inefficiency effects, 5

6 which are assumed to be independently distributed such that with mean,, and variance,, where is defined by the equation of the form is defined as normal distribution (2) The production function defined by eq.(1), specifies that the project and non-project farmers may have different mean levels of frontier output. The maximum-likelihood estimates for all the parameters of the stochastic frontier and inefficiency model, defined by Eqs. (1) and (2), are simultaneously estimated. (3) and (4) Where the - parameter has a value between 0 and 1, and means there is no inefficiency. The technical efficiency (TE) of production of the ith farmer, given the levels of inputs, is defined by (5) The technical efficiency of a farmer is between 0 and 1 and is inversely related to the level of the technical inefficiency effect. The technical efficiencies can also be predicted based on the maximum-likelihood estimator of the predictor for Eq. (5) that is based on its conditional expectation (Battese and Coelli, 1995). The stochastic frontier outputs, which include the effects of the random errors in production but not the technical inefficiencies of production, are important in comparing the productivity of farmers within the RCBP project and non-project groups. Given the specification of the stochastic frontier model in Eqs. (1) and (2), the stochastic frontier output for the ith farmer,, is the observed output divided by the technical efficiency,, i.e., 6

7 (6) Where represents the vector of values of the functions of the input variables in Eq. (1). The mean of the stochastic frontier output for the given input values for the ith farmer, is estimated by: (7) The above mean frontier outputs are estimated for the average input values for project and nonproject farmers in order to compare the overall productivity of the two groups of farmers Sample characteristics The determinants of farm household inefficiency are estimated using household characteristics such as household head age (years), household head level of education, gender of household head (1=male and 0=female), access to credit (1=yes, 0=no). Details of all variables are presented in Table 1. Table 1: Description of output, input and household specific variables Variable Ln output (Y) Input Ln cultivated area Ln labor Ln input cost Inefficiency variables Age Educ.- hh head Extension Gender Credit use Irrigation use Pesticide use Fertile land Description Natural log. of household total real crop output in quintals Farm size (ha) Number of adults in farming (adults) Input costs in birr Household head age (years) household head education (1 if none) access to extension services (1 if yes, 0 otherwise) Gender of household head (1 =male, and 0=female) Access to credit for farming (1 if yes, 0 otherwise) Use of irrigation (1 if yes, 0 otherwise) Pesticide use (1 if yes, 0 otherwise) Household having at least one fertile land (1 if yes, 0 otherwise) Inefficiencies (U i ) from equation (1) are linked with farm characteristic factors. We therefore expect age, education of the household head, access to extension, gender, credit use, irrigation 7

8 use, pesticide use and availability of a fertile land to have negative signs, as they are expected to decrease inefficiency in the inefficiency model Summary statistics output and input variables Table 2 presents the summary statistics of variables used in the stochastic frontier production analysis and the determinants for the farm household efficiency analysis. The table shows that there is no significant difference in the mean real output between project and non-project farmers. This is because farmers in the RCBP have the same fixed amount of land as non-rcbp farmers and the small land size constrained them from a higher use of labor and intermediate inputs to increase productivity. Table 2: Descriptive statistics of dependant and independent variables Project (N=351) Non-project (N=327) Variable Mean Std. Min Max Mean Std. Min Max dev dev Ln output (quintal) Ln labor (adults) Ln cultivated area (ha) Ln input cost (birr) Gender- hh head (1 if male) Age- hh head (years) Educ.- hh head (1 if none) Extension Credit use Irrigation use Pesticide use Fertile land Most household heads in both project and non-project are male: 72% for project and 69% for non project households. The average ages for farmers within the project was slightly less than for the farmers out-side the project. In the project group, 57% household heads and 70% of the nonproject group did not have an education. On average, non-project household heads were older that project group household heads. More of the project group had access to extension that the non project group (53% vs. 47%). More project participants use irrigation (17%) compared to non-project participants (13%). More non-project participants (29%) than project participants (25%) reported taking credit. Similarly, more (49%) non- project participants use pesticides than 8

9 project participants (31%). However, equal number of households (69%) from both groups reported having at least a piece of fertile land. 3. Empirical results and discussion Maximum-likelihood (ML) estimates of the parameters of the stochastic frontier production function were estimated using STATA software (version 11.0) (StataCorp. 2009). The stochastic production frontier model estimates and those for the technical inefficiency model for project and non-project farm households are presented in Table 3. Table 3: Maximum-likelihood estimation of the production frontier and determinants of technical inefficiency Variable Within Project Outside Project Stochastic frontier Coefficient Std. Err. Coefficient Std. Err. Constant (0.395) (0.442) Ln of labor (0.102)* (0.096)*** Ln of land (0.071)*** (0.062)*** Ln of input use (0.058)*** 0.38 (0.066)*** Inefficiency model + Constant ( ) (1.172) Gender (1.084) (0.549)* Age (0.031) (0.022) Education ( ) (0.587) Extension (0.879) (0.523)* Credit-use (0.836) (0.648) Irrigation-use (1.053) ( ) Pesticide-use (0.961) (0.505) Fertile land (0.965) (0.516) Variance parameters ( ) ( ) ( ) ( ) Log (likelihood) The coefficients in the inefficiency function are inefficiency effects and therefore a positive coefficient implies a negative effect on performance while a negative sign indicates a positive impact on efficiency; Significant at *P<0.05, **P<0.01, ***P<0.001 The output elasticities of inputs for both project and non-project farmers are positive. For both groups, the coefficients associated with labor, land, and input use were found to be positive and significant (P<0.001) (except for project group labor that was significant at P<0.05). For the project group, output elasticity of inputs was highest for land (0.444), followed by input use (0.362), and labor (0.222). In the non-project group, output elasticity of inputs was highest for 9

10 input use (0.38), followed by labor (0.292), and land (0.238). The results indicate that three inputs, land, labor, and input use have a major influence on agricultural output of both project and non-project farmers. In the inefficient model (Table 3), although statistically not significant, irrigation use, pesticide use, and having at least one piece of fertile land are negative indicating important factors to increase production efficiency in both the project group and non-project group. Gender, age, education, and credit use do not seem to be important factors affecting farm household efficiency in the project group. Although unexpected, one explanation of this empirical result could be due to the fact that the project had an explicit target of 40% of participants in the project group to be women-headed households. The unexpected sign of age could be due to the fact that farming is the most common activity in Ethiopia by all age groups. Similarly, the sign on education could be due to the fact that project participants do not necessarily need be educated as the extension services in the project woredas are trained to support all participating households. Additionally, the project group does not need to take a loan as some trial technologies are freely provided by the project. Unlike the project group, the negative and statistically significant (P<0.05) coefficients for gender and extension variables in the non-project group support the hypothesis that the gender of the head of household and access to extension services are important factors to increase production efficiency. The result on gender shows that female headed households were less efficient than male headed households in the non-project group. As the non-project group, this could be due to the lack of incentives for the extension services to target female headed households. Similar to the findings for Mozambique farming households by Uaiene and Arndt (2009), this could also be due to the fact that women spend substantial amounts of time doing households chores. The negative and significant (P<0.05) coefficient for extension, in the nonproject group, indicates the importance of smallholders accessing extension services. During the focus group discussions, female headed households indicated some reluctance by the extension services (development agents) in supporting women farmers. The negative sign on age shows that younger head of households are productive in the non-project group and hence important factor to increase production efficiency. Similarly, credit-use has a positive impact on efficiency in the non-project group. 10

11 The parameter γ, the estimate of the total error variance is , implying that 95% of the total variance is explained by the inefficiency effects. The high value of parameter γ highlights the importance of inefficiency effects in explaining the total variance in the model. The parameters associated with the variance of the technical inefficiency effects in the stochastic frontiers are estimated to be and for project and non-project farmers, respectively. These estimates indicate very little inefficiency for both groups of farmers. The mean predicted technical efficiency for farmers within the RCBP project was estimated to be with zero standard deviations, while for farmers outside the RCBP project, the mean technical efficiency was (Table 4). The pooled mean predicted technical efficiency was The fact that both groups have technical efficiency levels above 90%, suggests a relatively high level of efficiency. This implies that in 2009 both groups on average produced 99% of maximum attainable output given their current input usage and available technologies. These estimates indicate no substantial technical inefficiency in farming operations in both project and non-project farmers given the available technologies. Table 4: Farm efficiency descriptive statistics Variable Mean Std.dev. Min Max Efficiency RCBP project Efficiency non-rcbp project Efficiency combined The high efficiency in production of both groups of farmers could be the result of other previous projects similar to the RCBP implemented in Ethiopia to reduce poverty and support the enhancement of service delivery. These activities could have a long-term impact that could have spillover effects to other non-project farmers. This was corroborated by farmers during focus group discussions; farmers indicated their involvement in previous rural development interventions. 4. Conclusions We examined technical efficiencies of RCBP participating and non-participating households. The results indicated no significant difference in productivity changes and technical inefficiencies between project and non-project farmers, which could be explained by various 11

12 farm specific and household characteristic variables. The results show land, input use, and labor are important inputs and are strongly associated with total output. The mean technical efficiencies were calculated to be for both groups of farmers, indicating both groups being relatively efficient under their current resources, with little potential for reducing inputs or increasing outputs in the range of only 1%. The obtained measures of efficiency indicate that significant differences in productivity changes between project and nonproject households are not as great as expected by the project planners. The results suggest that in addition to its investment in Farmers Training Centers (FTCs), the government might have to address the technological and institutional constraints of its extension services. Additionally, since farmers are already at the edge of the frontier, government may consider innovative agri-business approaches and farm entrepreneurial development by providing a large number of services, including allowing private advisory services and guaranteeing output purchase. We note that the present study is based on data from a single production period: a follow-up is recommended to collect data to examine technical efficiency in Ethiopia. This would help policy makers in strengthening the capacity of and investing more in extension services. This is supported by the finding that in the non-project group, access to extension was associated with higher household farm efficiency. Similar to the findings of Seyoum et al. (1998) on productivity of maize farmers in Ethiopia, we find that access to extension services is important if farmers in Ethiopia are to significantly increase agricultural productivity. However, this should be supported by new improved technologies that will make a difference to peoples lives. 12

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