NMICS 2010 Report - Central Bureau of Statistics

NMICS 2010, Mid- and Far Western RegionsCharacteristics of female respondents aged 15–49 years and children under fiveTables HH.4 and HH.5 provide information on the background characteristics of female respondentsaged 15–49 years and children under five. In both tables, the total weighted and unweightednumbers of households are equal, since sample weights have been normalized (standardized).However, the weighted numbers for the Mid-Western Mountains and Far Western Mountains aremuch lower than the unweighted numbers due to oversampling in these two subregions.In addition to providing information on the background characteristics of women and children, thetables are also intended to show the numbers of observations in each background category. Thesecategories are used in subsequent tabulations in this report.Table HH.4 provides information on female respondents aged 15–49 years by region, subregion,urban/rural area, age, marital status, motherhood status, births in last two years, education andwealth index quintile 7 .Of total female respondents aged 15–49 years, 55 percent were living in the Mid-Western Regionand 45 percent were living in Far Western Region. Twenty-seven percent were in the Mid-WesternHills, followed by the Far Western Terai (25 percent), Mid-Western Terai (22 percent), Far WesternHills (13 percent), Far Western Mountains (seven percent) and Mid-Western Mountains (sixpercent). However, the weighted numbers for the Mid-Western Mountains and Far WesternMountains are much lower than the unweighted numbers due to oversampling in these twosubregions. Almost 89 percent of female respondents were from rural areas compared to only 11percent from urban areas.The 15–19-years age group had the largest proportion of women (21 percent), followed by the 20–24-years age group (19 percent), 25–29-years age group (17 percent), 30–34 years-age group (14percent), 35–39-years age group (12 percent), 40–44-years age group (11 percent) and 45–49-yearsage group (eight percent).A large proportion of surveyed women (77 percent) were married or in a marital union and aboutone fifth (20 percent) had never been married. Almost three quarters (73 percent) had given birth atleast once in their lifetime, and 17 percent had given birth at least once in the two years precedingthe survey. The majority of female respondents (55 percent) had never been to school, while 14percent had completed primary education, and 31 percent had completed secondary or highereducation.7Principal components analysis was performed by using information on the ownership of consumer goods, dwellingcharacteristics, water and sanitation, and other characteristics that are related to the household’s wealth to assign weights(factor scores) to each of the household assets. Each household was then assigned a wealth score based on these weightsand the assets owned by the household. The surveyed household population was then ranked according to the wealthscore for each household, and was divided into five equal parts (quintiles) from lowest (poorest) to highest (richest). Thefactors/assets used in these calculations were as follows: source of drinking water, type of sanitation facility, persons persleeping room, type of floor, type of roof, type of wall, type of cooking fuel, assets in households (electricity, radio,television, non-mobile telephone, refrigerator, improved cooking stove (ICS), table, chair, bed/cot, sofa, wardrobe,computer, wall clock, electric fan, dhiki/jato, microwave oven and washing machine) and assets of household members(watch, mobile phone, bicycle/rickshaw, motor cycle/scooter, animal-drawn cart, car/truck/bus/jeep, tractor, boat, rent,agricultural land, area of agricultural land, livestock and ownership of bank account.). The wealth index is assumed tocapture underlying long-term wealth through information on household assets, and is intended to produce a ranking ofhouseholds by wealth, from poorest to richest. The wealth index does not provide information on absolute poverty,current income or expenditure levels. The wealth scores calculated are applicable for only the particular dataset they arebased on. Further information on the construction of the wealth index can be found in: Filmer, D. and Pritchett, L., 2001.Estimating wealth effects without expenditure data – or tears: an application to educational enrolments in states of India.Demography 38(1): 115–132; Gwatkin, D.R., Rutstein, S., Johnson, K., Pande, R. and Wagstaff. A., 2000. Socio-EconomicDifferences in Health, Nutrition, and Population. HNP/Poverty Thematic Group, Washington, DC: World Bank; Rutstein, S.O.and Johnson, K., 2004. The DHS Wealth Index. DHS Comparative Reports No. 6. Calverton, Maryland: ORC Macro.33