Dependent Variable

Three dependent variables are selected in this study to estimate the major effects of agricultural extension that only a few studies have addressed successfully (Pudasaini, 1983). Most studies analyze one or two effects because of the lack of data such as household’s income and expenditure, and cost of inputs. Instead of measuring three effects of extension, a variety of studies use single crop production (Abdel-halim and Rowland 1976; Jamison and Lau, 1978). In order to examine the effects of extension on farm productivity, this study will use the following dependent variables provided in Table 9.

Table 9. Dependent Variables

Variables Explanation

Engineering Crop production

Gross revenue

(Maize, bean, and rice)

Market value of total farm production of crops and livestock

Value-added Gross farm revenue minus the cost of purchased inputs(Expenditures on farm and

livestock)

２９

Engineering function measures the impact on quantities of crop output, for example, kilograms of maize. The gross farm revenue includes the market value of farm crops and livestock. Specifically, gross revenues from crop yields and livestock are calculated by multiplying the quantities of crop yields and number of livestock multiplied by their unit cost. During the interviews of this study, households were asked to give information about the quantities of crop production, sales and home consumption, and unit price for each crop. Profit (Value-added) variable is required of the data of gross farm revenue and the cost of agricultural inputs.

3.3. Explanatory Variables

Three factors of production are considered in this study: land;

labor; and capital. Thus, vector X contains six variables: land used in crop yield; man-hours of hired labor employed; man-hours of family labor; Market value of agricultural assets of farmers;

schooling years of household head; and a number of extension contacts. Land was measured as cultivable size of land. Labor included two types of labors: men hired to work and household members working on the farm. Capital was measured as the value of agricultural assets and livestock. The studies reviewed on the extension and productivity control for factors that may affect the

３０

output by including variables such as schooling and research (Birkhaeuser et al., 1991). In this study, formal schooling-year is included.

Table 10. Explanatory Variables

3.4. Descriptive Statistics

Ninety-six men and eighty-five women were interviewed during the survey aged 18-65. Average income from agricultural activities is 581.1 dollars in 2015, and household income from livestock is 145.7 dollars in 2015 on average. The average size of the household is 4.63, including children and average land size for

Variables Explanation

Land Land used in crop yield

Hired Labor

Average man-hours of hired labor employed during a year

Family Labor Average man-hours of family labor during a year

Capital Market Value of assets for farming plus market value of livestock

Education Schooling years of household head Extension Number of Extension contacts

３１

cultivation is 1.58 acres. Man-hours of hired labor is about 416 hours a year. Man-hours of family labor is about 668 hours a year;

thus the average-hour of family labor during a year is higher than that of hired labor. The average schooling years of household head is about 9.92 years ranged from 0 to 17. This means that the average number of households in the district graduated from primary school and attended about two years of secondary school education. It is common that the farmers in Uganda have extension service. The number of extension contacts ranged from 1 to 30 and with an average of 4.86 contacts a year. The table 10 reported the descriptive statistics of this study.

３２

Table 11. Descriptive Statistics

Variable Description Mean SD

Dependent variable

ln Maize(kg) Log of Maize production(kg) 5.75 2.24

ln Bean(kg) Log of Bean production(kg) 4.49 2.16

ln VA Log of profit(gross farm revenue minus the expenditure of crops

and livestock) 13.96 1.67

Explanatory Variable

Land Size of Land for cultivation (acre) 1.58 1.07

Hired labor Man-hours of hired labor

employed (during a year) 416.20 469.62 Family labor Man-hours of family labor(during

a year) 667.59 451.79

Capital

(US dollar) Market value of farm assets

and livestock 682.04 936.15

Education

Household head’s schooling-year 9.92 4.30

Extension Number of extension contacts 4.58 5.76

N= 181

３３

Chapter 4. Model Specification and Estimation Method

4.1 Model Specification

According to the standard competitive theory, the perfect information assumption presents that the contribution to a factor is proportional, and marginal contribution to physical product is clear (Welch, 1970). However, for intangible factors such as education, it is difficult to measure the return to production. As a response to this constraint, past studies argued that other factors hold constant when the marginal product of education is estimated and emphasized the two values of education: worker effect and allocative effect. Worker effect is the marginal product of education, which refers to a worker’s capacity to yield more output, given education. Allocative effect is defined as the increased capacity to acquire right information about other inputs and to use them appropriately.

Welch (1970) emphasized the critical role of education as a factor of production. For the engineering function of a single commodity, we have a function of education where 𝑄 is physical output, 𝑋 is other inputs, and 𝐸 is education:

𝑄 = 𝑞(𝑋, 𝐸), (1)

３４

The marginal product of education, which indicates worker effect is therefore ^∂𝑞

∂𝐸. We also consider gross sales function where 𝑝₁ and 𝑝₂ are the prices of each commodity, 𝑞₁ and 𝑞₂. These commodities refer to a function of the input vector 𝑋 and the quantity of 𝑋 used in producing each commodity includes 𝑥₁ and 𝑥₂.

𝑄 = 𝑝₁𝑞₁(𝑥₁) + 𝑝₂𝑞₂(𝑥₂), (2)

Equation (3) is presented to maximization of sales, 𝑄:

𝜕𝑄

𝜕𝑥₁= 𝑝₁𝜕𝑞₁

𝜕𝑥₁− 𝑝₂𝜕𝑞₂

𝜕𝑥₂= 0 (3)

Note that a function of education is 𝑥₁= 𝑥₁(𝐸). Therefore, the marginal product of education,

𝜕𝑄

𝜕𝐸= (𝑝₁𝜕𝑞₁

𝜕𝑥₁− 𝑝₂𝜕𝑞₂

𝜕𝑥₂)𝑑𝑥₁ 𝑑𝐸 (4)

is positive if it increases allocative capability.

Lastly, value-added function which combines worker effect and allocative effects is presented as 𝑄 = 𝑝𝑞(𝑋, 𝑍) − 𝑝_𝑥𝑋, where physical product 𝑞₁ and 𝑞₂ are produced, and each is a function of three components: purchased inputs, X given inputs from individual farm, Z, and education 𝐸. The commodity prices can be expressed as 𝑝₁

３５

and 𝑝₂. Equation (5) shows value-added function by education and other given inputs: marginal product of education would be calculated as equation (6).

𝜕𝑄 allocating three factors, education, given inputs, and purchased inputs efficiently. The last term shows effect of selecting the appropriate quantity of purchased input, 𝑋.

３６

Following the model specifications, engineering production functions include only the worker effect. The gross revenue production functions reveal the worker effect as well as the effect of allocating inputs but exclude the effect of selecting the adequate quantities of inputs. Lastly, the profit or value-added production function includes all the returns. Earlier studies argued that allocative ability of education can be measured explicitly by the use of total derivatives or implicitly through value-added functions.

This study attempts to estimate the impact of not only formal education but also non-formal education, extension on farmers’

production, revenue, and profit in Mbale district.

4.2 Estimation Method

In studies of the effect of extension and education on agricultural performance, regression methods typically have implemented to estimate production functions (Phillips, 1994). The Cobb-Douglas below is the most common production function used in most studies.

ln𝑌 = 𝛼₀+ ∑ 𝛼_𝑖𝑙𝑛𝑋_𝑖+ 𝑏𝐸𝐷𝑈 + 𝑢, (7)

𝑌 is agricultural output, 𝑋 is a vector of agricultural inputs and

３７

control variables, 𝐸𝐷𝑈 is educational variable, and 𝑢 is the error term. The 𝑏 indicates coefficient, measuring the effect of extension on farm productivity.

The engineering function for Mbale district, was represented by maize, bean, and rice production functions. These three products were selected since they are the most dominant crops of Mbale, respectively. The specification of the following form is to be estimated for each crop:

𝑙𝑛𝑌 = 𝑙𝑛𝑐 + 𝛽_𝐿𝑙𝑛𝐿 + 𝛼_𝐻𝐿𝑙𝑛𝐻𝐿 + 𝛽_𝐹𝐿𝑙𝑛𝐹𝐿+ 𝛽_𝐶𝑙𝑛𝐶 (8) +𝛽_𝐸𝐷𝑈𝑙𝑛𝐸𝐷𝑈 + 𝛽_𝐸𝑋𝑙𝑛𝐸𝑋 + u,

In this function, 𝑌 is a quintal of maize, bean, and rice production in study area, respectively, 𝐿 is cultivated size of land for each crop, 𝐻𝐿 is man-hours of available hired labor for each crop during a year, 𝐹𝐿 is man-hours of available family labor for each crop during a year, 𝐶 is the market value of agricultural assets including livestock, 𝐸𝐷𝑈 is a continuous variable of household head’s schooling year, and 𝐸𝑋 is number of extension contacts during a recent year. All variables are expressed in logarithmic form.

In addition, gross revenue function is to be estimated in this study. The following form (9) indicates what variables included to

３８ estimate the function:

𝑙𝑛𝑌 = 𝑙𝑛𝑐 + 𝛽_𝐿𝑙𝑛𝐿+𝛼_𝐻𝐿𝑙𝑛𝐻𝐿 + 𝛽_𝐹𝐿𝑙𝑛𝐹𝐿+𝛽_𝐶𝑙𝑛𝐶 (9) +𝛽_𝐸𝐷𝑈𝑙𝑛𝐸𝐷𝑈 + 𝛽_𝐸𝑋𝑙𝑛𝐸𝑋 + u,

In gross revenue function, 𝑌 is gross farm revenue including the market value of each crop yields and livestock that each household owns. According to Pudasaini (1983), the gross farm revenue indicates market value of total farm production of crops and livestock, and off-farm earnings. However, this study excludes off-farm earnings.

The value-added function of the following form is to be estimated in this study:

𝑙𝑛𝑌 = 𝑙𝑛𝑐 + 𝛽_𝐿𝑙𝑛𝐿 + 𝛽_𝐹𝐿𝑙𝑛𝐹𝐿+ 𝛽_𝐶𝑙𝑛𝐶 (10) +𝛽_𝐸𝐷𝑈𝑙𝑛𝐸𝐷𝑈 + 𝛽_𝐸𝑋𝑙𝑛𝐸𝑋 + u,

where 𝑌 in value-added function is gross farm revenue minus the cost of purchased inputs, such as fertilizer and seeds. In this study, gross farm revenue minus farm and livestock expenses indicates dependent variable, 𝑌 . The explanatory variables exclude hired labor variable as it represents the cost of purchased input.

３９

Chapter 5. Empirical Results

5.1 The Estimates from Engineering Functions

This study firstly separated effects on farm output into three main single outputs: maize, bean, and rice production that most farmers in Mbale district grow for their home consumption and sales. A few estimates from the engineering functions had significant contributions to the maize production: land, man-hour of family labor during a year, and market value of the agricultural assets including livestock, which is called capital in this study. It indicates that these are important inputs in the maize cultivation.

Interestingly, hired labor had significant impact on each single output while family labor only contributed to maize production, indicating that each production mostly depends on hired labor. In addition, capital was a crucial input only in maize production.

Education is reported as positive in maize production. However, its coefficient in both bean and rice production were negative. As table 12 shows, education had no significant impact on each agricultural output. This suggests that household head’s education level may not be crucial in this environment. Meanwhile, extension had significant contributions with positive coefficients on both bean and rice output. The result means that extension may enhance

４０

farmer’s capacity to use several technologies required, especially for bean and rice cultivation. The estimated result of engineering production functions implies that extension service itself had no impact on the maize production while its contribution is significant both on bean output and rice cultivation. Since maize is acknowledged as the most dominant crop to consume in the study area, most farmers may already know how to produce as much as they possibly can. However, rice may be different from maize since bean and rice have a higher value in market in Mbale district.

Especially, rice is one of the cash crops in this region.

４１

Table 12. Estimates from Engineering Production Functions

Note: Number in parentheses are standard errors. ***, **, * designate significance at 1%, 5%, and 10%, respectively

Explanatory

４２

5.2 The Estimates from Gross Revenue and Profit Functions

First of all, the estimates from gross revenue production functions present that only land and extension variables made a significantly positive contribution on gross farm revenue. It is the general idea that if cultivated land size is larger, more output can be produced, and more products are sold in markets. Agricultural extension seems to be a crucial contributor in this environment as well. If household head attains more extension training during a year, his or her gross farm revenue will be higher in this context.

However, household head’s schooling year had no significant impact on gross revenue. It means informal education is more critical than formal education in terms of agricultural productivity in Mbale district.

The results from the profit production approaches indicate that land, education, and extension significantly contributed to household’s profit. In these functions, estimates of hired labor variable was excluded since it indicates the cost of input. Education, on the other hand, has negative coefficient with significant impact on the profit. Overall, education seems to have a weak influence and poor direct impact on agricultural activities. Even though education had no contributions to production and market activities, the

４３

estimation result indicates that more trained farmers through extension can enhance their ability to improve technical efficiency and make more profit.

Table 13. Estimates from Gross Revenue and Value-added Functions

Note: Number in parentheses are standard errors. ***, **, * designate significance at 1%, 5%, and 10%, respectively.

Explanatory

Variables Gross Farm Revenue Profit

Constant 4.145***

４４

Chapter 6. Summary and Conclusion

The primary objective of this research was to estimate effects of extension in Uganda and see if it raises agricultural productivity with the measurement of output referred by Welch (1970). This study also examined the contribution of education in rural areas in Mbale district of Uganda to farmers’ performance by estimating three production functions: engineering functions, gross revenue functions, and value-added functions.

Extension service is one of critical components of rural development, and has been presented to contribute to the reduction of hunger and poverty by increasing adoption of improved technologies, and capacity of individual farmers. The agricultural technical information delivered by extension enables the farmers to increase capability to adopt new technologies and inputs. In Uganda, agricultural extension has reformed over the time period through a number of implementations in order to increase agricultural performance by empowering farmers to demand them. According to Gautam (2000), impact of agricultural extension on higher return in the economy has been low and evaluated as an ineffective strategy to raise agricultural productivity throughout Africa. In the case of Uganda, the reputation of extension was the same with the

４５

evaluation of its program in Africa. Faced with ineffective results from extension service, the government of Uganda reformed the design in NAADS in 2008, with an aim of transferring effective agricultural extension services to farmers in rural areas (Betz, 2009). Following this shift from ineffective program to effective extension delivery, this research found that delivering extension service at least in the study area are significantly effective on output increases, gross farm revenue, and profit.

The estimated results from engineering, gross revenue, and profit production functions showed that agricultural extension service had a significantly positive impact on bean and rice production, gross farm revenue, and profit except for maize output.

The results indicate extension is the critical factor of enhancing farmer’s direct productivity to the agricultural output. Furthermore, agricultural extension seems to be the important contributor in generating more income and profit. The result presented is that if household’s head access to extension increases, his or her gross farm revenue and profit increases.

This study also estimated the effects of education in Uganda, using household’s data from the survey. A number of past studies found empirical evidences that education raise agricultural output.

Lockheed et al. (1980) noted that crop output increased by 7

４６

percent by four years of primary schooling. However, existing evidence on the effect of education on farm performance in Africa was not sufficient. Only few studies estimated the worker effect and allocative effect of the education on agriculture in sub-Saharan Africa countries. In addition, the impact of education on return in Africa was evaluated as an ineffective strategy to raise agricultural productivity, with the insignificant estimates. The results of this study showed that education has limited impacts on farm production and gross farm revenue as various literatures indicated. It only significantly contributed to agricultural profit. Nevertheless, negative coefficient on education did not lend much support for hypothesis of this study. As a result, decomposing effects into worker, input-allocative, and input-selection effects and comparing the marginal effects remained key challenges of the study.

According to the previous literature, each coefficient of education indicates the marginal value product estimated from production functions (Pudasaini, 1983). Following by the method of estimating each effects of education, three worker effects from each engineering functions can be calculated. In addition, input-allocation effect and input-selection effect can be computed from gross revenue functions, Lastly, the total effects were able to be measured from value-added functions. The entire process enables

４７

us to acknowledge which effects are the most crucial to each product function. However, estimating three effects of education in the study area remained a difficult task since the results from each production functions except profit production functions presented that education was ineffective on agricultural activities, Instead, extension had a significant impact on each production functions except maize production. Thus, additional research is required in order to explain further effects of educations.

This research conducted the survey in Uganda in 2016 concerning the effect of extension service and education on farm productivity. In the data sets, most of the estimated results of the formal education were found to have a negative and insignificant effect. On the other hand, extension was revealed to be a more critical contributor than formal education in terms of agricultural productivity in Mbale district of Uganda. This positive result that emerges from the findings is that at least extension in the study areas overcomes many challenges such as lack of response to farmers’ needs and non-participatory approaches. Though the estimated results for the formal education was mostly negative or statistically insignificant, the uncontrolled effect of the education such as farmers’ capacity to learn more techniques and various skills could have a positive contribution to farm productivity.

４８

Generally, the estimation of the effects of education is a complicated task since there are many factors that affect the agricultural production. In addition, education itself is difficult to measure accurately because the effects of education can be cumulative. The researcher found the ground of the result in the real circumstances in East Africa. The education is likely to face the following constraints: lack of funding to have more capital, insufficient number of teachers, unsatisfactory qualities of classroom, lack of transport, lack of materials to teach. Thus, impact of education on farmer’s livelihood in Mbale district might be able to capture only a few portions of the overall contribution in econometric studies. However, this study contributes to analyzing effects of agricultural extension and education on farm productivity in Africa country and revealing extension to be a more critical contributor than formal education in terms of agricultural productivity in Mbale district.

４９

References

ABDEL‐HALIM, A. A., & Rowland, K. M. (1976). Some personality determinantsof the effects of participation: A further investigation. Personnel Psychology, 29(1), 41-55.

Anderson, J., & Van Crowder, L. (2000). The present and future of public sector extension in Africa: contracting out or contracting in?. Public Administrationand Development, 20(5), 373-384. productivity: evidence from Uganda. University of Oxford, Centre for the Study of African Economies.

Betz, M. (2009, July). The effectiveness of agricultural extension with respect to farm size: the case of Uganda. In Paper provided by Agricultural and Applied Economics Association in its series 2009 Annual Meeting.

Birkhaeuser, D., Evenson, R. E., & Feder, G. (1991). The economic impact of agricultural extension: A review. Economic development and cultural change, 39(3), 607-650.

Elias, A., Nohmi, M., Yasunobu, K., & Ishida, A. (2013). Effect of Agricultural Extension Program on Smallholders' Farm Productivity: Evidence from Three Peasant Associations in

５０

the Highlands of Ethiopia. Journal of Agricultural Science, 5(8), 163.

Evenson, R. E., Pray, C., & Rosegrant, M. W. (1998). Agricultural research and productivity growth in India (Vol. 109). Intl Food Policy Res Inst.

Fane, G. (1975). Education and the managerial efficiency of farmers.

The Review of Economics and Statistics, 452-461.

Gautam, M. (2000). Agricultural extension: The Kenya experience:

An impact evaluation. World Bank Publications.

An impact evaluation. World Bank Publications.

문서에서 Effects of Agricultural Extension Service and Education on the Farm Productivity - Evidence from Mbale District in Uganda - (페이지 34-0)