RURAL BANGLADESH - PreventionWeb

Socioeconomic Profile Findings
households. Female-headed households are also burdened with a higher dependency ratio;
coupled with fewer potential income earning individuals within the household, they face
more difficulties in diversifying household income sources and therefore realizing livelihood
or food security. Significant differences in literacy rates by sex of household head, discussed
in the Education section below, shows a further disadvantage faced by female household
heads.
Table 3: Demographic characteristics of Survey Households
WFP Regions N HH size Dependency ratio Age of HH
head
Illiterate
CHT 448 5.13 90.31 44.00 44.0
Coastal 437 5.42 95.97 43.00 51.5
Drought 439 4.21 62.67 42.90 54.7
N/W 438 4.56 76.88 42.47 47.9
Char 440 4.62 76.31 42.92 58.0
Haor
Sex of HH head
443 4.64 90.61 41.83 56.9
Female 155 3.01 87.61 46.06 74.8
Male 2506 4.88 81.85 42.68 50.7
1.2 SOCIOECONOMIC HOUSEHOLD PROFILES
This section profiles the four socioeconomic categories of households discussed in the
report. The process employed to create the socioeconomic categories of households is
discussed below. The four groups or socioeconomic classes of households include:
♦ ‘Non-vulnerable’ HH;
♦ ‘On the Edge’ HH – presented as Group 2 in some of the tables;
♦ ‘Vulnerable’ HH – presented as Group 3 in some of the tables; and
♦ ‘Invisible poor’ households – presented as Most Vulnerable in the tables.
1.2.1 Constructing the Household Socioeconomic Profiles
TANGO applied a two-step process to create socioeconomic profiles, beginning with the
selection of five indicators, which together could explain vulnerability and food security.
The five indicators include:
a) Number of month’s of access to adequate food (food security)
b) Dietary diversity (number of food groups acquired in week)
c) Number of meals eaten in 24 hours
d) Household income (Monthly household expenditure)
e) Household assets (value of assets in taka)
Using Principal Component Analysis, TANGO extracted components from the five variables
or indicators to explain the most variation. The analysis resulted in one principal component
that explained 46 percent of the variation within the five variables used in the analysis.
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