M.T. Ahmed*
Bangladesh Academy for Rural Development (BARD), Kotbari, Comilla, Bangladesh
H. Bhandari
International Rice Research Institute, Bangladesh
P.U. Gordoncillo
Department of Agricultural and Applied Economics, College of Economics and Management, University of the Philippines Los Baños, Laguna, College 4031, Philippines
C.B. Quicoy
Department of Agricultural and Applied Economics, College of Economics and Management, University of the Philippines Los Baños, Laguna, College 4031, Philippines
G.P. Carnaje
Department of Economics, University of the Philippines Los Banos, Laguna, College 4031, Philippines
Livelihood diversification, Simpson index, Tobit regression, Rural Bangladesh.
Narsingdi, Madaripur, Mymensingh, Bogra, Comilla, Chandpur, Chuadanga, Jhenaidah, Patuakhali, Kurigram and Thakurgaon
Socio-economic and Policy
Income generation
Data source and sampling design This study was conducted in 12 villages representing major agro-ecologies and diverse livelihoods of Bangladesh. Eleven districts were selected purposively to represent large geographical area and diverse livelihoods of the country. Those districts are Narsingdi, Madaripur, Mymensingh, Bogra, Comilla, Chandpur, Chuadanga, Jhenaidah, Patuakhali, Kurigram and Thakurgaon. Multi-stage random sampling technique was followed to select sample villages. In 10 districts, one subdistrict from each district, one union from each sub-district and one village from each union were selected randomly. In Mymensingh district, which is the 5th largest district in the country (Wikipedia, 2018), two sub-districts, one union from each subdistrict, and one village from each union were selected randomly. Thus, 12 villages were randomly selected from 11 districts and four geographical regions (e.g. northern region, middle region, south-eastern region and south-western region) of the country. Finally, 45 rural households were randomly chosen from each selected village making a total sample of 540 households. Only 500 households were included in the analysis as some households’ data were incomplete. The study used primary data collected through face to face interview using pre-tested semi-structured questionnaires during 2012–2013. The collected information included demography , land ownership, primary and secondary occupations of household members, migrations and remittances, assets ownership, labor force, on farm activities, off-farm activities, non-farm activities, credit and savings, agricultural prices, income from different sources and living conditions to name major ones. The most important determinant of livelihood for any society is income. In this study, household income refers to net income generated by deducting total cost from total return. The share of income from different sources was the basis to assess their livelihood diversification. Extra attention was paid during data collection and analysis to estimate household’s income accurately because farmers do not keep record about their crop production related data and often they tend to underreport their income. Sometimes they do not consider their own production and the inkind received as income. Household income was grouped into nine sources. 1) Rice crop (net income from all rice crops in a year); 2) Non-rice crops (net income from all non-rice crops in a year); 3) Non-crop agriculture (income from livestock, fishery and forestry); 4) Agricultural labourer (labour employed in agricultural sectors); 5) Non-agricultural labourer (included both formal and informal types of employment); 6) Petty business; 7) Salaried job and services; 8) Remittance income (received from family members presently living outside the family: both domestic and abroad); and 9) Transfer payment For analysing purpose sampled households were also classified in four groups based on their landholding. (1) Functionally landless (>= 0.2 ha), (2) Small (0.21-0.80 ha), (3) Medium (0.81-1.50 ha) and (4) Large (>1.50 ha). Analytical tools Simple descriptive analysis (average, mean, median, percentage, etc.) was carried out to determine the household income from different sources. Tabular analysis was done to find the share of various income sources and the extent of livelihood diversification. Tobit multiplicative heteroscedasticity regression was employed to determine the factors affecting the extent of livelihood diversification. The Microsoft Excel and STATA-12 was used to analysis the data. Part I: Extent of livelihood diversification The most common measure of livelihood diversification is the vector of income share associated with different income sources. Livelihood diversification can be measured using different indicators and indices, such as Simpson index, Herfindahl index, Ogive index, Entropy index, Modified Entropy index and Composite Entropy index. Several studies have used the Simpson index to measure livelihood diversification. This study followed the suite because of its computational simplicity, robustness and wider applicability.
SAARC J. Agri., 16(1): 7-21 (2018)
Journal