Characteristics of Households - Childinfo.org

Table HH.4R provides background characteristics offemale respondents aged between 15–49 years. Thetable includes information on the distribution of womenaccording to residence, age, marital status, motherhoodstatus, births in the two years preceding the survey,education 6 and wealth index quintiles.Approximately, 69 percent of interviewed eligible womenlive in urban areas. The age distribution of women between25–49 is similar to the distribution of the overall population.Almost 77 percent of all women in this sample are married,while 14 percent have never been married. Distributionby motherhood is similar to marital status: 81 percent ofwomen have given birth. The majority of interviewed womenhave primary education (68 percent), while the proportion ofwomen with no education is 17 percent. Overall, 14 percentof women between 15–49 have secondary education, andonly 1 percent have higher education. As far as wealth indexquintiles are concerned, less women live in householdswithin the poorest (19 percent) and second quintile (19percent), while the majority of women live in householdswithin the richest quintile (about 21 percent of women).Table HH.4R.M: Men’s background characteristics, Roma Settlements, 2010Percent and frequency distribution of men age 15–29 years by selected background characteristicsWeighted Number of menpercent Weighted UnweightedAreaUrban 68.2 598 540Rural 31.8 279 337Age15–19 33.7 295 24720–24 33.4 293 30225–29 32.9 289 328Marital/Union statusCurrently married/in union 54.6 478 572Formerly married/in union 6.5 57 41Never married/in union 39.0 342 264Weighted Number of menpercent Weighted UnweightedEducationNone 7.6 66 83Primary 68.3 599 631Secondary 23.1 202 153Higher 1.0 9 10Wealth index quintilePoorest 21.8 191 218Second 18.9 166 180Middle 19.6 172 164Fourth 21.1 185 179Richest 18.6 163 136Total 100.0 877 877Table HH.4R.M provides background characteristics ofmale respondents between 15–29 years of age. The tableincludes information on the distribution of men accordingto residence, age, marital status, education and wealthindex quintiles 7 .6Unless otherwise stated, throughout this report “education” refers to the educational level attained by the respondent when it is used as a background variable.7A principal components analysis was performed by using information on the ownership of consumer goods, dwelling characteristics, water and sanitation, andother characteristics that are related to the household’s wealth to assign weights (factor scores) to each of the household assets. Each household was thenassigned a wealth score based on these weights and the assets owned by that household. The survey household population was then ranked according tothe wealth score of the household they are living in, and was finally divided into 5 equal parts (quintiles) from lowest (poorest) to highest (richest). The assetsused in these calculations were as follows: type of water and sanitation, number of rooms for sleeping per member; main material of dwelling floor, roof andexterior walls; the type of fuel used for cooking; presence in household of electricity, radio, television, non-mobile telephone, refrigerator, electric stove, bed,table with chairs, vacuum cleaner, PC/Laptop, closet, washing machine, drying machine, air conditioner, jacuzzi tub and video monitoring system; possessionby household members of watch, mobile telephone, bicycle, motorcycle or scooter, animal-drawn cart, car or truck, boat with motor, tractor; and ownership ofbank accounts by members of the household. The wealth index is assumed to capture the underlying long-term wealth through information on the householdassets, and is intended to produce a ranking of households by wealth, from poorest to richest. The wealth index does not provide information on absolutepoverty, current income or expenditure levels. The wealth scores calculated are applicable for only the particular data set they are based on. Further informationon the construction of the wealth index can be found in Filmer, D. and Pritchett, L., 2001. “Estimating wealth effects without expenditure data — or tears: Anapplication 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-Economic Differences in Health, Nutrition, and Population. HNP/Poverty Thematic Group, Washington, DC: World Bank. Rutstein, S. O. andJohnson, K., 2004. The DHS Wealth Index. DHS Comparative Reports No. 6. Calverton, Maryland: ORC Macro.MONITORING THE SITUATION OF CHILDREN AND WOMEN 43