5. A STUDY ON THE IMPACT OF PERSONAL EFFICACY ON JOB MOTIVATION AND JOB SATISFACTION AMONG WORKING WOMEN WITH SPECIAL REFERENCE TO THOSE WHO ARE STAYING AWAY FROM HOME

The objective of the study was to analyze the impact of women empowerment on agricultural productivity. Cross sectional data were collected from a total of 150 randomly selected respondents. Seven key indicators of empowerment combining both quantitative and qualitative data and four dimensions of empowerment were chosen for this study. A cumulative empowerment index (CEI) was developed by adding the obtained weighted scores of the seven empowerment indicators based on which the respondents were classified into empowered and non-empowered women. The distribution of CEI demonstrates that 38.7% of women were empowered at varied levels of empowerment whereas, 61.3% of the women were concentrated in non-empowerment category. Cobb-Douglas (CD) production function was applied to estimate the agricultural productivity difference in between the two groups. Results of the study show that empowered women farmer (EWF) own more productive resources such as land, oxen, labor and other agricultural inputs as compared to non-empowered women farmer (NEWF). The estimate of CD production function show that oxen, herbicide use, land size, male and female labor was statistically significant for both EWF and NEWF. The comparison of the Marginal Value Product (MVP) with the factor cost shows that EWF and NEWF could increase productivity using more herbicides, male and female labor. The agricultural productivity difference between EWF and NEWF was about 68.83% in the study area. However, if NEWF had equal access to the input as EWF, gross value of the output would be higher by 23.58% for NEWF. Land size, oxen and seed difference between EWF and NEWF made about 9.1%, 7.73% and 6.6% productivity difference in agriculture, respectively. KEYWORDS Women empowerment, Agricultural productivity, Household, Ethiopia. INTRODUCTION hough agriculture is the largest contributor to overall economic growth in Ethiopia, it is dominated by small scale farmer with subsistence farming system in low productive and highly degraded lands (AGP, 2010). Ethiopia accounts about 88% of the country’s women live in the rural area, and nearly 85% women work in agricultural activities like food processing, storage, weeding, harvesting, marketing, preparing threshing field and caring for animals (Bogalech, 2000). In general, women’s contributions in rural Ethiopia have remained invisible, especially the female headed households are more invisible to research, donors and policy makers (Tiruwork 1998; Addis, 2000). These situations have put women at a disadvantageous position with respect to agricultural resources. Low participation of women in economic activities has a negative impact on the realization of the Plan for Accelerated and Sustained Development to End Poverty (PASDEP). The objectives of this study are to analyze the impact of women empowerment on agricultural productivity in Kersa district of East Hararghe zone of Oromiya Regional State, Ethiopia. STUDY AREA AND DATA COLLECTION Kersa district of Oromiya Regional State, located at 475km east of Addis Ababa, the capital city of Ethiopia. The district has three major crops production, these are cereal, vegetable and Khat based products. Both primary and secondary data collected from Kersa district were used in the study. Primary data was collected from sample households through structured survey questionnaire covered information on women empowerment, demographic and farm characteristics, crop and livestock production, household income and ownership of farm inputs. The secondary data collected from Agriculture and Rural Development Office, Education Bureau, Women Affair Office, and Administration Office of the district. The participatory assessment methods such as Focus Group Discussions, case studies and key informant interviews were applied to gather information pertinent to the research problem. A two stage random sampling technique was used to select the sample households in the study area. The first stage was simple random sampling of 6 FAs from the 35 FAs found in the district. Then from these 6 FAs 150 households were randomly selected and interviewed. Equal proportion of female headed and male headed households were included in the sample. Both descriptive and econometric analyses were employed to meet the specific objectives of the study. In this study descriptive statistics such as mean, frequency, percentage, t-test, chisquare were used to analyze the collected data and compare the empowered and non-empowered women. MEASUREMENT OF EMPOWERMENT Cumulative Empowerment Index (CEI) The CEI is a composite of seven empowerment indicators combining, both quantitative and qaulitative data in order to get a comprehensive feature of women’s empowerment. The quantitative part represents six categories (e.g., 0=No, 1=very low, 5=very high) which has been done on the basis of total obtained score for each empowerment indicator from the survey. The qualitative dimension stems out of the total weighted scores indicated by the six enumerators (where 7 denotes very important and 1 denotes less important). Thus, a total of 77 specific attributes were added together to develop CEI inorder to understand the economic, socio-cultural, legal and political dimensions of women’s empowerment (Table 1). Cobb-Douglas Production Function Cobb-Douglas (CD) production function was used to examine the agricultural productivity difference between the empowered and non empowered women farmers. According to Gujarati (1995), the generalized form of the CD production function can be specified as: i n U B n B B B e X X X AX Y ........, ,......... 3 2 1 3 2 1 = Where, Y is gross value of farm outputs in Birr per ha, Xi ’ s are explanatory variables such as land size, oxen, seed, fertilizer use, herbicides use, male or female labor and capital. Bi’s are coefficients or elasticities of output and indicates how strongly each input affects output. A is efficiency parameter and represents the level/state of technology and Ui is disturbance term. Production function for Empowered Women Farmer represented as: e e e e e e e e e e e U X B X B X B X B A Y + + + + + + = 7 7 3 3 2 2 1 1 ln .... ln ln ln ln ln Production function for Non-Empowered Women Farmer represented as: ne ne ne ne ne ne ne ne ne e n e n U X B X B X B X B A Y + + + + + + = 7 7 3 3 2 2 1 1 ln .... ln ln ln ln ln Production function using pooled data represented as: T VOLUME NO. 5 (2015), ISSUE NO. 02 (FEBRUARY) ISSN 2231-4245 INTERNATIONAL JOURNAL OF RESEARCH IN COMMERCE, ECONOMICS & MANAGEMENT A Monthly Double-Blind Peer Reviewed (Refereed/Juried) Open Access International e-Journal Included in the International Serial Directories http://ijrcm.org.in/ 86 p p p p p p p p p p p U X B X B X B X B A Y + + + + + + = 7 7 3 3 2 2 1 1 ln .... ln ln ln ln ln Production function using pooled data with dummy empowerment variable is: p p p p p p p p p p p U DE X B X B X B X B A Y + + + + + + + = 7 7 3 3 2 2 1 1 ln .... ln ln ln ln ln Where, e = EWF, ne = NEWF, p = pooled data set, E = Empowerment dummy variable (E = 1 for empowered women; E = 0 otherwise) and D is the regression coefficient for the dummy variable and it indicates productivity difference in technical efficiency. B ie, B ine and B ip (i = 1, 2, 3, ..., 7) are output elasticities of i input under EWF, NEWF and pooled data sets, respectively. The MVP of the factor can be computed as follows; i i X Y b MVP * = Where, bi is the regression coefficient (output elasticity), Y is the gross value of farm output (geometric mean) and Xi the geometric mean value for factor i (Ellis, 1988). Finally, Oaxaca decomposition model (Oaxaca, 1973) of the productivity differential between empowered and non-empowered women farmers were used to decompose the productivity difference. Although, this approach was used to decompose the income gap, it can also be applied to decompose productivity difference between, say, men and women farmers (Quisumbing, 1995). The decomposition model adopted was presented as follows: Ln ( ) [ ]             + − =       ine ie ie ine ine ie ne e X X Ln B LnX B B Y Y Where, Ye and Yne represent mean output (geometric mean) of empowered and non-empowered women farmer respectively, Xie and Xine are geometric mean levels of inputs of empowered and non-empowered women farmer, Bie and Bine are estimated output elasticities of empowered and non-empowered women farmer as defined earlier. Estimation Technique and Testing Procedures The Variance Inflation Factor (VIF) was estimated by following the method of Gujarati (1995), which is:         − = 2 1 1