Kamala Gurung
Practical Action in Nepal, Maharajgunj, Kathmandu, Nepal
Humnath Bhandari
International Rice Research Institute, Banani, Dhaka, Bangladesh.
Thelma Paris
International Rice Research Institute, Los Baños, Laguna, Philippines
Food security, Livelihood, Land use, Gender roles, Gender equity, Women’s participation, Bangladesh
Khulna, Satkhira, and Mymensingh
Farming System
Livelihood
Site Selection This study was conducted in three districts of Bangladesh, namely, Khulna, Satkhira, and Mymensingh, where CAF has become popular over the past few decades. These three districts were purposefully selected for this study in keeping with its objectives. Table 1 presents the salient features of these districts. Mymensingh has a larger land area, population, and cropping intensity than Satkhira and Khulna, but its rice yield is slightly lower. Both rice farming and aquaculture are practiced in all three districts. Sampling Design A purposive stratified random sampling technique was used to select the sampling units. Ten villages comprising three each from Khulna and Satkhira districts and four villages from Mymensingh district were randomly selected from the strata of dominant rice farming and aquaculture villages. Of the 10 villages, seven were dominated by CAF, while three were mostly dedicated to rice farming. Some 40 house-holds were selected randomly from each village for the survey. Data Collection The primary data were collected using a combination of qualitative and quantitative methods. Qualitative information was gathered with the help of participatory rural appraisal tools such as 10 separate focus group discussions (FGDs) each for groups of men and women, self-evaluation of decision making, self-evaluation of access to and control over resources, a timeline of land-use system, and well-being ranking.A pretested semi-structured questionnaire was used to gather quantitative data related to rice production and CAF, primarily focusing on demographic characteristics, farm size, land-use patterns, livelihood sources, costs and returns, gender-disaggregated labor allocation, and gendered access to and control over resources and activities. The primary data were collected from December 2011 to April 2012. Data Analysis Wealth ranking and self-assessment methods were used to analyze the qualitative information. Wealth Ranking Method The sampled farm households were classified into four socioeconomic groups based on the wealth-ranking criteria identified through the male and female group discussions. Table 3 shows the multiple criteria used to classify households into rich, upper-middle, lower-middle, and poor socioeconomic groups. Typically, economists classify households into socioeconomic groups, using a single criterion of farm size. But rural households believe that many non-land resources and activities are important to determine the living standard of a household and they should also be taken into account. Therefore, we classified households into socioeconomic groups using the wealth-ranking method, which takes into account multiple criteria and the perception of farmers. Self-assessment Method This tool allows participants to assess their status in terms of resource endowment and access to resources and activities (e.g., markets, credit, and other services) and explain any changes in gender relations within and beyond the household (O’kane & Feinstein, 2005; Orsmond, Merry, & Reiling, 2000). Based on the consensus of the men’s and women’s groups, each rice or aquaculture resource and activity was given a score from 0 to 10 to reflect each group’s level of involvement in the decision-making process of agricultural activities and access to and control over resources. For example, the self-rated scores of 0–2 = no, 3–5 = limited, 6–7 = moderate, and 8–10 = strong are the levels of involvement in the decision-making process and access to and control over resources and activities. The average self-evaluated score of decision making and access to and control over resources and activities was derived separately for men and women by taking the average of 10 FGDs of the two groups. These self-rated scores are presented in the spider web graph.Gini Coefficient and Lorenz Curve The Gini coefficient (or the Gini index or Gini ratio) is a measure of statistical dispersion intended to represent the income distribution of a nation’s residents and is the most commonly used measure of inequality. It was developed by the Italian statistician and sociologist Corrado Gini and published in his 1912 paper “Variability and Mutability” (Italian: Variabilità e mutabilità) (Ceriani & Verme, 2011). The Lorenz curve and Gini coefficient are the most common methods of analyzing income distribution and income inequality, respectively (Deininger & Squire, 1996; Thewissen, 2014). We used these two tools to assess the income distribution and inequality between households with and without aquaculture. Costs and Returns Analysis We analyzed the cost and return of rice cultivation by collecting the data for the largest parcel of the sampled households. Likewise, we analyzed the cost and return of aquaculture by collecting the data for the largest pond of the sampled households. Sex-disaggregated data were collected to analyze male and female labor participation in rice farming and aquaculture. The data were analyzed using the SPSS computer software package.
Gender, Technology and Development 20(1) 49–80
Journal