Decomposition of Gender Differentials in Agricultural Productivity: The Case Aira District in West Wollega Zone Oromia National Regional State Ethiopia

The research is financed by Ministry of Education of Ethiopia in general and Wollega University particularly Abstract This study focused on decomposion of gender differential on agricultural productivity. The agricultural productivity difference between male and female headed households was about 57.18% in the study area. On the other hand, if female headed households had equal access to the inputs as male headed households, gross value of the output would be higher by 18.82% for female headed households. This may suggest that female headed households would have been more productive than male headed households if they had equal access to inputs as male headed households. Thus accessing female headed households to inputs that increase the productivity of land, labour utilization, usage of herbicide; and introducing technologies that reduce the time and energy of women is essential to improve the agricultural productivity of women and the society as a whole.

female headed households is also increasing from time to time since death of husband, divorced problem and males are migrating to other areas in search of better employment leaving behind their wives and children. Thus, females would take over the position of their husband in addition to their routine household management. Therefore, it is essential to study the productivity of female headed households in agricultural sector as compared with their male counterparts in the area where gender dis-aggregated information in this aspect is missing.

RESEARCH METHODOLOGY 2.1. Description of the study area
Aira district is one of the twenty three districts found in West Wollega zone of Oromiya Regional State. It is located at a distance of 532 km from Finfinne, the capital city of the country. The capital town of the zone is Gimbi which is 94 km from the district. It is bounded by Gulliso in the north, Yubdo in southeast and Kelem Wollega Zone in the south and west. Aira is the administrative center.
Currently the total population of the district is 60,971 out of which females account for 46.84%. The community in the district comprises of a total of 7719 households (11.92% female headed households). The average family size is estimated to be 6 persons per household and the average population density is 217 per km2 .The population of Aira district is almost exclusively Oromo constituting 99% of the population. The rest 1% of the ethnic groups are Guraghe and Amhara. The religions of the district are Orthodox Christian (31.98%), Protestant (59.84%), Muslim (5.59%) and traditional beliefs (1.19%) of the population (CSA, 2019 of the Aira district).
Agriculture is the dominant economic activity engaging 90% of the labour force (CSA, 2019 of the district). Crop production is dependent on rainfall and the major crops produced in the area according to their importance are Maize (26% of total area cultivated), Millet (24%), sorghum (10%), Teff (6%), barely (4%) and pulses (horse bean, field pea and lentil covering about 11% of the total cultivated area). Coffee is one of most cash crops grown in the woreda and the most source of income for the population of the woreda. Productivity of these crops is below the national average due to poor fertility of soil, poor agronomic practices and use of low level of agricultural inputs.

Type, Source of Data and Methods of Data Collection
Both qualitative and quantitative data were collected from primary and secondary sources. The qualitative data were collected through focus group discussion and participatory observation. The quantitative data were gathered by using of structured questionnaire.

Sampling Method and Sample Size Determination
This study employed probability sampling procedure to draw a representative sample. Two stage sampling procedure were used to select sample households. In the first stage, about 6 kebeles were selected randomly from 20 Kebeles found in the district. In the second stage from these 6 Kebeles 60 male and 60 female headed households are randomly selected. The total sample from each kebeles, male and female headed households is determined by fifty (50) to fifty (50) ratios respectively due to the lower number of female headed households in the data to define the proportion. Probability proportional to sample size was employed to select the total of sampled households' farmers. Hence, a total of 120 households were selected (table 1). The sample size was determined by using the formula given by Yamane (1967: 886) as follow: Where: −Represents sample size −Represents total number Male headed and Female Headed −Represents the desired level of precision (taking 9%)

Methods of Data Analysis
Oaxaca-Blinder decomposition model of the productivity differential between male and female farmers was used to decompose the productivity difference (Oaxaca, 1973). Although this approach is to decompose the wage gap, it can also be applied to decompose productivity difference between, say, men and women farmers (Shambel, 2013;Tadele and Mahendran, 2015). The decomposition model adopted was presented as follows: (2) Where:-Ym and Yf represent mean output (Geometric mean) of males and females respectively Xim and Xif are geometric mean levels of inputs of male and female Bim and Bif are estimate of output elasticities of male and female headed households as defined earlier. The model decomposes the overall average male-female output gap into the portion due to differences in the technical efficiency and the portion attributable to differences in input endowments. In other words, the first bracketed expression on the right hand side is a measure of change in output due to shift in output elasticities of the production functions. The second bracketed term is a measure of difference in output due to difference in volume of input use per hectare.

Source of productivity difference
This section presents estimates of the agricultural productivity differences between male and female headed households using decomposition model. As discussed in section 2.4, this model is helpful to measure the percentage contribution of the different to agricultural productivity difference between male and female headed households. This method allows distinguishing the productivity difference that can be explained by differences in household endowments and differences in the efficiency of these endowments. In addition to the estimates of production functions, the decomposition analysis requires the geometric mean values of different inputs and output. Table 2 presents geometric mean values of various inputs and output in both MHH and FHH. The geometric mean of output and input endowments were computed from the explanatory variable before converting to natural logaresim since geometric mean does not compute negative values in the data. It is observed that the inputs used by MHH were higher as compared to FHH for all the explanatory variables used in the model. Source: Own survey result (2019) By following the methodology described in the section 2.4 (equation 2), the total sources of productivity difference were decomposed into output elasticities and input endowments (table 3). Source: Own survey (2019) As shown in the above table it can be seen that the total productivity difference in agriculture between the two groups was about 57.18%. However, they have different human capital, endowment and different access to factors and inputs as discussed in the descriptive part. Inputs use differentials accounted for 76.02%. This implies that the productivity could be increased by 76.02%, if the FHH could adjust their inputs to the same level of MHH. On the other hand, the difference in output elasticities was -18.82%. This indicates that productivity difference as the result of difference in output elasticities is greater for MHH as compared to that of FHH. The result was relatively confirmed with Tadele and Mahendran (2015) in their study of gender differences and its impact on agricultural productivity in the case of Sheko district in Benchi Maji Zone of SNNP, Ethiopia.
A comprehensive assessment of the contributions made by different inputs in the total productivity gap between male and female headed households reveals that difference in access to land use caused the biggest bound. This further indicates that if FHH could adjust their farm land to the level of MHH, they can increase their productivity by about 57.18%. Hence, increasing the access of FHH to farm land could highly increase their productivity in agriculture in the study area. Descriptive results of this study also show that on average FHH had only 2.05 mean of land size while MHH had about 2.80 mean of land size on average, which was significant at 1% probability level (t=1.71). And also inorganic fertilizer, labour, livestock holding, farming experiences, Non-farm income and amount of credit use contributes difference between MHH and FHH made about 17.4%, 9.8%, 1%, 0.5%,0.09% and 0.04% productivity difference in agriculture, respectively (table 3).
Most researchers often argued that women's lack of access to resources results in lower productivity or inability to respond to economic incentives (Shambel, 2013;Tadele and Mahendran, 2015). Looking at the contribution made by the output elasticities or change in factor specific productivity, herbicides used is one of the variables which contribute largely to output elasticities or change in factor specific productivity difference. Which constitutes 83% to the total output difference followed by fertilizer used and improved seed in which they reduce International Journal of African and Asian Studies www.iiste.org ISSN 2409-6938 An International Peer-reviewed Journal Vol.60, 2020 5 the diffence in output gap by 68.7% and 51.3% respectively. Number of oxen and non-farm/off-farm income contributes output gap between MHH and FHH by 33.7% and 2.8% correspondingly.

SUMMARY AND CONCLUSION
The study examined on decomposition of gender differentials in agricultural productivity: The case of Aira District in West Wollega Zone Oromia National Regional State Ethiopia. The data used in this study were collected from 60 MHH and 60 FHH randomly selected from 6 Kebeles of the district. Independent t-test was used to test the differences between MHH and FHH in terms of continuous variables and ! -test for categorical variables. Moreover, decomposion Model was estimated to measure productivity difference between MHH and FHH.
The total productivity difference in agriculture between the two groups was about 57.18%. However, they have different human capital, endowment and different access to factors and inputs. Inputs use differentials accounted for 57.18%. This implies that the productivity could be increased by 76.02%, if the FHH could adjust their inputs to the same level of MHH. On the other hand, the difference in output elasticities was-18.82%. This indicates that productivity difference as the result of difference in output elasticities is greater for MHH as compared to that of FHH. The 2017 Revision. Available at www.worldometrers.info info/ world-population/Ethiopia-population/). Yamane Taro, 1967.Statistics: An introductory analysis, 2 nd Ed., New York: Harper and Row