DEMOGRAPHIC

AND

HEALTH

RESEARCH

DHS WORKING PAPERS

2010 No. 77

Fikrewold H. Bitew

Daniel S. Telake

November 2010

This document was produced for review by the United States Agency for 

International Development.

Undernutrition among Women in Ethiopia
The DHS Working Papers series is a prepublication series of papers reporting on research in progress that 

is based on Demographic and Health Surveys (DHS) data. This research is carried out with support 

provided by the United States Agency for International Development (USAID) through the MEASURE 

DHS project (#GPO-C-00-08-00008-00). The views expressed are those of the authors and do not 

necessarily reflect the views of USAID or the United States Government. 

MEASURE DHS assists countries worldwide in the collection and use of data to monitor and evaluate 

population, health, and nutrition programs. Additional information about the MEASURE DHS project can 

be obtained by contacting MEASURE DHS, ICF Macro, 11785 Beltsville Drive, Suite 300, Calverton, 

MD 20705 (telephone: 301-572-0200; fax: 301-572-0999; e-mail: reports@measuredhs.com; internet: 

www.measuredhs.com).
Undernutrition among Women in Ethiopia: Rural-Urban Disparity 

Fikrewold H. Bitew 

Daniel S. Telake 

ICF Macro

Calverton, Maryland, USA

November 2010 

Corresponding author: Fikrewold H. Bitew, Ethiopian Public Health Association (EPHA), P.O. Box: 150 434,

Addis Ababa, Ethiopia; E-mail: fikre.wold@gmail.com
Suggested citation: 

Bitew, Fikrewold H. and Daniel S. Telake. 2010. Undernutrition among Women in Ethiopia: 

Rural-Urban Disparity. DHS Working Papers No. 77. Calverton, Maryland, USA: ICF Macro. 
ABSTRACT 

The Millennium Development Goal of reducing maternal mortality by three-quarters 

between 1990 and 2015 (MDG 5) is highly related to women’s nutritional status. Poor maternal 

nutrition is directly associated with mother’s lack of resistance to infection and to maternal ill 

health during pregnancy and childbirth, particularly among the poor. Therefore, providing 

obstetric care alone is not enough unless poor women’s nutritional status is also addressed. Indepth understanding of women’s nutritional status is crucial to reducing maternal mortality and 

food insecurity. The percentage undernourished is higher in Ethiopia than in any other subSaharan African country. Thus this study focuses on undernutrition in Ethiopia to assess levels 

and socio-demographic differentials between rural and urban areas, and to analyze determinants. 

The paper uses the population-level data sets from the 2000 and 2005 Ethiopian 

Demographic and Health Surveys (EDHS), comprising 13,057and 5,677 non-pregnant and nonpostpartum women age 15-49, respectively. Women’s body mass index (BMI) (kg/m2

) is used as 

a measure of women’s nutritional status, and those with a BMI value less than 18.5 are 

considered to be at risk of chronic energy deficiency (CED). A logistic regression model was 

employed to identify important determinant factors of women’s undernutrition. 

The study finds that 30.5% of women in the 2000 EDHS and 26.9% of the women in the 

2005 survey were undernourished, of whom in the 2000 survey 19.4% were moderately 

undernourished and 11.1% were severely undernourished. Similarly, in the 2005 survey 18.0% 

were moderately undernourished and 8.9% severely undernourished. The levels of undernutrition 

were almost 1.5 times higher for rural than urban women. There seems to be some improvement 

in women’s undernutrition status between 2000 and 2005. 

Age, marital status, religion, occupation, wealth index and region of residence were 

found to significantly affect chronic energy deficiency for women in 2000 survey year, while in 

2005 religion and region of residence were no longer factors influencing women’s 

undernutrition. In 2000 undernutrition in rural areas was significantly associated with age, 

marital status, occupation and region, while in 2005 marital status was no longer a determinant. 

In urban areas, marital status, education (in 2000 only), wealth and region were found to 

significantly affect women’s level of undernutrition in the two survey years. Among the regions, 
women in Tigray, Afar, Somali, Gambella and Ben-Gumuz were highly likely to be 

undernourished. In these regions more than one-third of women experience undernutrition. 

In order to improve women’s nutritional status, policy should focus on creating 

mechanisms and opportunities to increase agricultural productivity, particularly in Tigray, Afar, 

Gambella, Ben-Gumuz and Somali, and to promote health service provision in these regions. 

Policy also should emphasize women’s education, particularly in rural areas, and seek job 

creation opportunities particularly in urban areas. 
1

INTRODUCTION 

Undernutrition and poor health from preventable causes disproportionately affect the 

well-being of millions of people in the developing world. Factors at individual, household and 

community levels, or a combination of these factors, may contribute to poor nutrition and health 

status. In particular, malnutrition among women is likely to have a major impact on their own 

health as well as their children’s health. More than 3.5 million women and children under age 

five in developing countries die each year due to the underlying cause of undernutrition (Robert 

et al., 2008). 

Intensified actions to improve nutrition in these countries are needed to achieve the 

Millennium Development Goal of halving severe hunger by 2015 (MDG 1) and also to greatly 

increase the chances of achieving the Millennium Development Goal of reducing maternal 

mortality by three-quarters between 1990 and 2015 (MDG 5). In order to resolve the problem of 

maternal mortality, most researchers recommend the provision of emergency obstetric services 

by including the use of skilled birth attendants and effective referral systems (Ronsmans et al., 

2008). Although providing improved obstetric care is very important, it alone cannot be enough 

to resolve the problem. Attending the nutritional needs of women in their childbearing age is an 

equally important aspect of improving women’s/mother’s survivorship. 

According to the 2009 special report of the Crop and Food Security Assessment Mission 

to Ethiopia (FAO/WFP, 2009), 7.5 million persons are still chronically food insecure and were 

under the productive safety net programme (PSNP); an additional 4.9 million people are facing 

acute food insecurity, as of January to June 2009. 

Undernutrition is a serious problem in Ethiopia, and women and children are the most 

affected segments of the population. One of every four (26%) women of reproductive age in 

Ethiopia is undernourished (Central Statistical Authority [Ethiopia] and ORC Macro, 2006), 

twice the sub-Saharan average of 13.3% (Mukuria et al., 2005). Comparing the nutritional status 

of Ethiopian women with that of women in 29 other sub-Saharan African countries, based on the 

most recent DHS conducted between 1998 and 2008, the prevalence of undernutrition in 

Ethiopia is higher than in any other country (Figure 1). 
2

Only limited research has been conducted on the levels and determinants of maternal 

nutritional status in Ethiopia (Lindet and Tadesse, 1997; Woldemariam and Timotiwos, 2002; 

Teller and Yimer, 2000; Mulugeta et al., 2009). This may be due to lack of relevant data at the 

national level and to the limited number of researchers in the area. Some studies focus on only 

rural geographical settings (Lindet and Tadesse, 1997) to show intra-household correlations of 

nutritional status. Others focus on a particular region to show levels and determinants of 

malnutrition among young and adult women (Teller and Yimer, 2000). Still others examine a 

single older survey to show the determinants of malnutrition among women in Ethiopia 

(Woldemariam and Timotiwos, 2002). A recent study on the nutritional status of adolescent girls 

from rural communities of Tigray, Northern Ethiopia (Mulgeta et al., 2009) has helped to close 

the research gap in the area, although it is still focused on a small geographic area and small 

populations. The present study, which looked at the levels, differentials and determinants of 

undernutrition by examining the associated rural-urban disparity, therefore is an important 

contribution to minimize the research gap. 

2

4

6 6 7

7

8 8 8 8

8 8 9 9

10 11 11 11

12 12 12 12 12

15 16

17

19

21

23

24

0

5

1 0

1 5

2 0

2 5

Figure 1: Percentage of women whose BMI is less than 18.5 in sub-Saharan 

Africa: DHS 1998-2007

Percentage
3

More considerably, the study has important policy implications from a global health 

perspective as well as a country-specific viewpoint. From a global health perspective it will help 

to evaluate the progress being made by countries towards achieving the Millennium 

Development Goals. At the country level, governments would be better informed about where to 

allocate their scarce resources in their effort to improve the nutritional and health status of their 

general population, and of women in particular. As this study aims to analyze DHS data from 

two time points (year 2000 and 2005), our analysis will provide trends at national and regional 

levels, which is crucial to monitor the impact of any intervention made so far. At the same time, 

the study will help to identify regions of Ethiopia where the problem of undernutrition is 

relatively severe. 
4

DATA AND METHODS 

Data 

In this analysis we used data collected in the two rounds of the Ethiopia Demographic 

and Health Surveys (EDHS), which were carried out in 2000 and 2005. The surveys were 

designed to provide estimates for health and demographic variables for the following domains: 

Ethiopia as a whole; urban and rural areas of Ethiopia (each as a separate domain); and 11 

geographic areas (nine regions and two city administrations), as follows: Tigray; Affar; Amhara; 

Oromia; Somali; Benishangul-Gumuz; Southern Nations; Nationalities and Peoples (SNNP); 

Gambela; Harari; Addis Ababa; and Dire Dawa. The response rates for eligible women age 15-

49 were 98% in 2000 and 96% in 2005.

The indicator used to assess undernutrition among women is body mass index (BMI), 

also known as Quetlet index. The analysis for BMI is based on 18,734 (13,057 in 2000 and 5,677 

in 2005) non-pregnant and non-postpartum women. The sampling design and implementation 

procedures for both surveys are described in detail in the two EDHS final reports (Central 

Statistical Agency [Ethiopia] and ORC MACRO, 2001 and 2006). 

Measures 

Among four possible anthropometric indicators to assess women’s chronic 

undernutrition—height less than 145 cm; body mass index (BMI) < 18.5 (thinness); weight less 

than 45 kg; and mid-arm circumference (MUAC) < 22.5 cm (Victoria Quinnin, 1999)—BMI was 

used as the outcome for this study. BMI is defined as weight in kilograms divided by height 

squared in meters (kg/m2). Weight was measured by using a solar-powered scale, called 

Uniscale, with an accuracy of ± 100g, and height was measured with an adjustable wooden 

measuring board, called Shorr Height boards, measuring to the nearest 0.1cm. In order to avoid 

the impact of fetus and lactation on BMI, the study avoided pregnant women and women less 

than or equal to three months postpartum (women who had a birth in the three months preceding 

the survey). 
5

Methods 

This study applied the WHO classifications for chronic undernutrition using BMI to 

assess nutritional status: normal >=18.5; thin 17.0-18.4; moderately thin 16.0-16.9; and severely 

thin <=15.9 (ACC/SCN, 1992 and 2000). Undernutrition in the analysis therefore refers to 

BMI<18.5. The estimates to examine the differentials and relative risk factors among rural and 

urban areas were conducted by classifying women into urban/rural parts over the two 

consecutive DHS surveys (2000 and 2005). The demographic and socioeconomic variables used 

in the analysis were: age; parity; current marital status; education and occupation (of the 

respondents and their partners); place of residence; region of residence; wealth index; and 

religion, using different classifications1

.

The study used univariate, bivariate, and multivariate analyses. Univariate analysis refers 

to simple descriptive statistics of all theoretically relevant variables. In the bivariate analysis 

trends over the two survey years were examined at different levels of undernutrition (thin, 

moderately thin and severely thin) by various independent variables. The association between the 

dependent variable (whether the women were undernourished or not) 2

and each of the 

explanatory variables were conducted using Pearson’s chi square test. The relative regional 

differences in the undernutrition levels were described using a map of Ethiopia (see Figure 5). 

For the multivariate analysis we fitted logistic regression models to identify the relative 

contribution of selected independent variables in explaining the total variance in women’s 

nutritional status. The svy set syntax was employed while running the logistic regression model 

to take into account sampling of the 2000 and 2005 surveys. Six different models were fitted to 

identify factors affecting the rural/urban/total women separately over the years. Stata 10 software 

was used for analysis. 

 

1

 Wealth index was classified into two categories; those in the lower wealth quintiles (1 to 4) were classified as nonrich; those in the highest quintile (5) were classified as rich. 

2

 The dependent variable (undernutrition) was classified into two categories and coded as: BMI<18.5 and >=18.5.
6

RESULTS 

Characteristics of the Respondents 

Table 1 presents the percentage distribution of non-pregnant and non-postpartum women 

age 15-49 according to the selected demographic and socioeconomic characteristics. The 

distribution of women by background characteristics looks similar over the years (2000 and 

2005). In our sample a large proportion (80.7%) of women were selected from rural areas and 

19.3% from urban areas over the two years. Regionally (Figure 2), the majority of the samples 

were selected from Oromia region (38.1% for 2000 and 34.4% for 2005), followed by Amhara 

(25.0% and 24.8%) and SNNP (21.2% and 21.0%). The rest of the regions contributed less than 

10% of the total sample. The majority of the women (73.7% for 2000 and 63.2% for 2005) had 

no education, while most others (16.6% and 23.8%) had completed primary education. The rest 

(9.7% and 13.0%) had secondary education or higher. Most of the women (51.3% in 2000 and 

49.9% in 2005) were Orthodox Christians, while about a quarter (28.4% and 28.4%) were 

Muslims and most others (15.7% and 18.1%) were Protestants. Women with other religions were 

less than 5%. Women who engaged in agricultural activity were 47.1% of the sample in 2000 

and 23.0% in 2005. In 2000, 36.3% were in the unemployed/missing and don’t know categories, 

while in 2005 the comparable figure was 63.4%. Another 16.6% in 2000, and 13.6% in 2005, 

were in non-manual/professional jobs, mostly in urban areas. Three fourths of the women (74.5% 

in 2000 and 73.0% in 2005) were in the non-rich category and the rest (25.5% and 27.0%) were 

classified as rich. Almost all of the non-rich (98.9% and 98.3%) were from rural areas and most 

of the rich (72.5% and 66.5%) were from urban parts of the country (Table 1).
7

Table 1: Percent distribution of women age 15-49 by background characteristics in Ethiopia: 

urban, rural and total, EDHS 2000 and 2005

Background Characteristics 

2000 2005

Urban Rural Total Urban Rural Total 

Age

15-19 30.5 25.1 26.1 29.6 25.2 26.1

20-29 35.9 31.9 32.7 37.2 32.3 33.3

30-39 20.2 22.8 22.3 19.6 23.6 22.9

40-49 13.4 20.2 18.9 13.5 18.8 17.8

Parity

0 49.1 32.0 35.3 53.6 30.7 35.1

1-2 20.9 18.4 18.9 22.5 17.0 18.1

3-4 12.7 15.1 14.7 11.4 16.9 15.9

5+ 17.2 34.5 31.1 12.5 35.3 30.9

Marital Status

Never married 43.1 24.2 27.8 47.5 24.9 29.2

Married /Living together 38.3 63.3 58.4 35.1 64.3 58.7

Widowed/Divorced/Separated 18.6 12.6 13.7 17.4 10.8 12.1

Education 

No education 34.0 83.3 73.7 23.1 72.8 63.2

Primary education 24.6 14.6 16.6 25.6 23.4 23.8

Secondary and above 41.5 2.1 9.7 51.3 3.8 13.0

Religion 

Orthodox 65.4 47.9 51.3 73.2 44.4 49.9

Muslim 21.3 30.1 28.4 14.8 31.7 28.4

Protestant 11.2 16.7 15.7 10.4 19.9 18.1

Others 2.2 5.3 4.7 1.6 4.0 3.6

Occupation 

Agricultural/Manual(Skilled/unskilled) 20.3 53.5 47.1 10.9 25.9 23.0

Non-manual/Professional 36.2 11.9 16.6 32.2 9.1 13.6

Unemployed/Missing and don't know 43.6 34.6 36.3 56.9 65.0 63.4

Wealth Index 

Non-rich 4.4 91.3 74.5 6.5 88.8 73.0

Rich 95.6 8.7 25.5 93.5 11.2 27.0

Partner's Education 

No education 18.0 55.4 48.2 14.2 50.3 43.3

Primary 11.8 15.6 14.9 10.4 19.3 17.6

Secondary and above 26.2 4.1 8.4 27.1 4.9 9.2

Missing and don't know 44.0 24.9 28.6 48.2 25.5 29.9

Partner's Occupation 

Unemployed 0.6 0.4 0.4 1.3 0.6 0.7

Agricultural 9.7 68.8 57.4 7.6 69.3 57.5

Manual 16.6 2.0 4.8 13.5 1.4 3.7

Non-manual/Professional 29.0 4.5 9.3 28.6 3.4 8.3

Missing and don't know 44.1 24.3 28.1 48.9 25.3 29.8

Total 19.3 80.7 19.3 80.7

Number of Cases 2,526 10,531 13,057 1,094 4,584 5,677

Note: Higher proportion of missing cases happened for Partner’s characteristics since only women who have been 

ever married are asked about their partners. 
8

Levels of Undernutrition 

Table 2 presents the mean BMI scores and BMI scores among women at different levels 

of undernutrition (severely thin; moderately thin; and thin) by important background 

characteristics, including rural or urban residence and region of the country. Mean BMI scores 

showed very small progress between the two surveys, from 19.8 in 2000 to 20.3 in 2005. In line 

with this the level of undernutrition (BMI <18.5) showed a decline from 30.5% to 26.9%. 

Women residing in rural areas constituted the majority of the proportion undernourised (85% in 

2000 and 86% in 2005). Considering the degrees of undernutrition, most of the women were in 

the mildly thin category, BMI=17-18.4 (19.4% in 2000, 18.0% in 2005), followed by moderately 

thin (6.8%, 5.4%) and severely thin (4.3%, 3.5%), among all chronically under-nourished 

women. As mentioned, the majority of the undernourished women were in rural areas, of which 

group most were mildly thin (20.5%, 19.5%), while others were moderately thin (6.8%, 5.4%) or 

severely thin (4.3%, 3.5%). 

0 0 1

1 1 1

5

6

21

25

38

0 0 1 1 1

3

6

7

22

25

34

0

5

1 0

1 5

2 0

2 5

3 0

3 5

4 0

Figure 2: Percent distribution of women by region of residence, 

EDHS 2000 and 2005 

2000

2005

Percent
9

Among the regions, women in Somali, Ben-Gumuz, Amhara, Tigray and Gambella were 

most affected by chronic undernutrition. Regions with the highest prevalence of severely thin 

women include Somali (8.6%, 8.5%), Afar (7.8%, 5.08%), Tigray (4.7%, 5.6%) and Gambella 

(5.9%, 5.3%). Also, never-married women and women with no children (parity 0) were most 

affected by severe undernutrition (see Table 2). 

Table 2: Mean BMI and levels of undernutrition using different cutoff points for women age 15-49 

by background characteristics in Ethiopia, EDHS 2000 and 2005

Background 

Characteristics 

BMI (Kg/M2)

2000 2005

Mean 

BMI

Severely 

thin

(% BMI 

< =15.9)

Moderately 

thin

(% BMI 

16.0-16.9)

Mildly 

thin

(% BMI 

17.0-18.4)

Mean 

BMI

Severely 

thin

(% BMI 

< =15.9)

Moderately 

thin

(% BMI 

16.0-16.9)

Mildly 

thin

(% BMI 

17.0-18.4)

Residence 

Urban 20.95 3.21 5.15 14.81 21.65 2.29 4.84 11.79

Rural 19.56 4.55 7.23 20.48 19.92 3.73 5.54 19.50

Region

Tigray 19.72 4.73 8.17 22.01 19.51 5.59 10.37 22.07

Afar 19.72 7.84 9.80 24.18 20.02 5.08 8.47 18.64

Amhara 19.57 4.77 7.22 19.91 19.93 4.26 5.61 17.74

Oromia 19.82 3.62 6.41 18.96 20.39 2.20 4.25 18.21

Somali 19.24 8.57 12.86 27.14 20.05 8.47 9.52 18.52

Ben-Gumuz 19.20 6.82 9.85 22.73 19.54 3.92 7.84 23.53

SNNPR 19.77 4.85 6.73 19.66 20.18 3.28 5.12 18.73

Gambella 19.23 5.88 11.76 20.59 19.65 5.26 10.53 21.05

Harari 20.91 2.94 5.88 14.71 20.97 0.00 6.25 12.50

Addis Ababa 21.78 1.56 3.73 12.44 22.18 2.47 3.09 9.88

Dire Dawa 20.98 2.86 8.57 15.71 21.32 3.33 6.67 13.33

Parity

0 19.79 7.79 8.41 17.87 20.19 5.71 7.57 15.34

1-2 19.99 2.11 5.72 18.16 20.54 2.05 4.09 17.15

3-4 19.89 1.72 5.12 21.16 20.72 1.56 3.78 16.46

5+ 19.76 2.85 6.49 21.03 19.92 2.62 4.56 22.38

Marital Status

Never married 19.66 8.75 9.16 17.74 20.06 5.78 8.01 14.94

Married /

Living together 19.86 2.32 5.68 20.29 20.32 2.46 4.17 19.08

Widowed/

Divorced/

Separated 20.05 3.62 6.97 18.84 20.42 2.63 5.12 20.32

Total 19.83 4.29 6.82 19.38 20.25 3.45 5.41 18.02
10

Patterns of Undernutrition 

Figure 3 shows the percent distribution of women age 15-49 according to levels of 

undernutrition, in rural and urban areas, based on BMI scores for 2000 and 2005. The shape of 

the graph looks skewed to the right, indicating that most women were concentrated in groups 

with lower BMI scores. The graphs show an increasing level in the BMI scores from <16 to 20-

25, and then the direction is reversed to lower rates through BMI scores from 20-25 to 35+. For 

the lower BMI scores (<16 to 20-25), rural women show a relatively higher level than their urban 

counterparts, and for the higher BMI scores urban women show a relatively higher level of 

nutritional status than rural women. 

0

1 0

2 0

3 0

4 0

5 0

6 0

Figure 3: Percent distribution of women age 15-49 according to BMI scores, 

EDHS 2000 and 2005 

2000 urban 2000 rural 2005 urban 2005 rural Percent
11

Bivariate Analysis of Differentials in Undernutrition 

Table 3 presents the percentage differentials of undernutrition (BMI<18.5) among 

women by selected socioeconomic and demographic characteristics, comparing rural and urban 

areas over the two surveys. The results of the bivariate analysis using Pearson’s chi square test 

showed significant association between all explanatory variables and women’s undernutrition in 

2000; however marital status and religion were not significantly associated with undernutrition in 

2005. In urban areas the background characteristics that had a significant effect on women’s 

undernutrition were age in 2005, marital status in 2005, wealth in both 2000 and 2005, and 

partner’s education in 2005. In rural areas all the underlying characteristics except women’s 

education were significantly associated with women’s undernutrition. 

Demographic Differentials of Undernutrition 

Women’s age 

The magnitude of chronic energy deficiency among women at different ages reveals how 

nutritional status varies during the reproductive years. Among all women age 15-49, the 

prevalence of chronic energy deficiency by age shows that women age 15-19 and 40-49 were 

most affected, with prevalence of 38.4% in 2000 and 33.0% in 2005 among women age 15-19, 

and prevalence of 34.9% in 2000 and 30.9% in 2005 among women age 40-49 (Table 3). 
Table 3: Urban-rural differentials in undernutrition (percentage of women age 15-49 with BMI<18.5) by background 

characteristics in Ethiopia: urban, rural and total, EDHS 2000 and 2005

Background Characteristics 

2000 2005

Urban N Rural N Total N Urban N Rural N Total N

Age

15-19 24.5 770 42.4 2,640 38.4 3,410 22.2 324 36.0 1,156 33.0 1,480

20-29 20.4 906 25.1 3,356 24.2 4,263 18.4 407 21.9 1,482 21.1 1,889

30-39 21.3 511 28.0 2,403 26.8 2,915 18.7 214 26.5 1,083 25.2 1,298

40-49 30.2 338 35.7 2,132 34.9 2,470 13.5 148 33.8 863 30.9 1,011

p-value 0.099 * 0.000 *** 0.000 *** 0.298 0.000 *** 0.000 ***

Parity

0 22.9 1,241 38.2 3,370 34.1 4,612 20.5 585 31.9 1,409 28.6 1,995

1-2 21.0 528 27.3 1,938 26.0 2,467 17.8 247 25.1 780 23.4 1,026

3-4 22.4 321 29.1 1,592 28.0 1,914 12.0 125 23.5 775 21.8 900

5+ 27.1 435 30.8 3,630 30.4 4,065 20.6 136 30.3 1,620 29.6 1,756

p-value 0.458 0.000 *** 0.000 *** 0.272 0.002 *** 0.001 ***

Marital Status

Never married 23.5 1,089 40.8 2,546 35.7 3,635 21.7 520 31.9 1,140 28.7 1,660

Married /Living together 20.6 967 29.4 6,661 28.3 7,629 13.3 384 27.3 2,950 25.7 3,333

Widowed/Divorced/Separated 27.7 469 30.0 1,324 29.4 1,793 22.5 191 30.3 495 28.1 685

p-value 0.102 0.000 *** 0.000 *** 0.016 ** 0.057 * 0.171

Education 

No education 26.0 858 31.9 8,767 31.3 9,625 20.9 253 28.4 3,338 27.9 1,660

Primary education 19.5 621 34.9 1,540 30.5 2,161 19.6 280 30.8 1,071 28.5 3,333

Secondary and above 23.1 1,048 28.7 223 24.1 1,271 17.6 562 24.1 174 19.0 685

p-value 0.132 0.127 0.002 *** 0.619 0.244 0.001 ***

Religion 

Orthodox 22.9 1,652 31.5 5,040 29.4 6,692 19.5 800 28.4 2,035 25.9 2,835

Muslim 25.5 537 34.6 3,168 33.3 3,706 16.0 162 31.0 1,451 29.5 1,613

Protestant 19.9 281 29.8 1,763 28.4 2,045 18.4 114 26.9 914 26.0 1,027

Others 23.6 55 33.4 560 32.6 615 22.2 18 25.0 184 24.8 202

p-value 0.505 0.173 0.045 ** 0.846 0.333 0.228

Cont’d..

12
Table 3: Cont’d

Background Characteristics 

2000 2005

Urban N Rural N Total N Urban N Rural N Total N

Occupation 

Agricultural/Manual(skilled/unskilled) 26.2 512 30.6 5,638 30.2 6,150 23.5 120 26.9 1,187 26.6 1,307

Non-manual/Professional 19.2 914 28.0 1,251 24.3 2,165 16.5 352 23.8 418 20.5 770

Unemployed/Missing/Don't know 25.0 1,100 36.4 3,642 33.7 4,742 19.4 622 30.2 2,979 28.3 3,601

p-value 0.195 0.000 *** 0.000 *** 0.186 0.058 * 0.003 ***

Wealth Index 

Non-rich 36.6 112 32.5 9,616 32.5 9,728 35.2 71 29.4 4,071 29.5 4,143

Rich 22.6 2,415 30.0 915 24.6 3,330 17.8 1,023 23.6 512 19.7 1,535

p-value 0.052 * 0.264 ns 0.000 *** 0.000 *** 0.054 * 0.000 ***

Partner's Education 

No education 26.9 454 29.8 5,833 29.6 6,288 20.0 155 29.0 2,305 28.5 2,460

Primary 21.7 299 29.6 1,640 28.4 1,939 22.8 114 25.4 883 25.1 997

Secondary and above 20.8 663 26.0 435 22.9 1,097 11.1 297 21.2 226 15.5 522

Missing and don't know 23.6 1,111 40.4 2,622 35.4 3,733 22.2 527 32.3 1,170 29.2 1,698

p-value 0.388 0.000 *** 0.000 *** 0.011 ** 0.009 *** 0.000 ***

Partner's Occupation 

Agricultural/Manual(skilled/unskilled) 24.2 665 29.7 7,458 29.3 8,122 18.0 231 27.9 3,242 27.3 3,473

Non-manual/Professional 21.9 734 25.7 474 23.4 1,208 15.5 313 24.1 156 18.4 469

Unemployed/Missing/Don't know 23.5 1,128 40.8 2,599 35.5 3,727 21.2 549 31.7 1,186 28.4 1,735

p-value 0.704 0.000 *** 0.000 *** 0.241 0.071 * 0.004 ***

Total 23.2 2,526 32.3 10,531 30.5 13,057 18.9 1,094 28.8 4,584 26.9 5,677

Note: *** significant at p<0.01;** significant at p<0.05; * significant at p<0.1; unmarked = not significant

13
14

Figure 4 shows the level of women’s undernutrition by age groups, for rural and urban 

women over the years 2000 and 2005. A relatively similar pattern was observed among rural 

women as among the total group of women over the two survey years. Among rural women age 

15-19, 42.4% in 2000 and 36.0% in 2005 were chronically undernourished. Among rural women 

age 40-49, 35.7% in 2000 and 33.8% in 2005 were chronically undernourished. Among urban 

women age 15-19, chronic undernutition was lower; still, almost one-fourth (24.2% in 2000 and 

22.0% in 2005) were undernourished. Examining the changes between 2000 and 2005 in levels 

of undernutrition by age group, chronic undernourishment remained relatively high except 

among older urban women. Unlike among other groups, the level of undernutrition among urban 

women age 35 and older showed a sharply decreasing trend (Figure 4).

0

5

1 0

1 5

2 0

2 5

3 0

3 5

4 0

4 5

1 5-1 9 2 0-2 4 2 5-2 9 3 0-3 4 3 5-3 9 4 0-4 4 4 5-4 9

Figure 4: Percentage of women with undernutrition (BMI<18.5) by age in 

Ethiopia: urban and rural, EDHS 2000 and 2005

2000 urban 2000 rural 2005 urban 2005 rural Percent
15

Parity 

The number of children ever born (parity) was another important factor found to 

significantly affect women’s nutritional status. Results of Table 3 show that women who have 

never had a child (parity 0) and women with at least five children (parity 5+) were at a higher 

risk of chronic energy deficiency than other women. As expected, chronic energy deficiency by 

parity shows that for all parities undernutrition was much higher among rural than urban women. 

 

Marital status 

Never-married women were found to be the most affected by undernutrition, followed by 

divorced/separated/widowed women. Among never-married women, 35.7% in 2000 and 28.7% 

in 2005 were chronically undernourished. Among both rural and urban women for both surveys, 

those married or living together were the least affected by chronic energy deficiency (Table 3). 

Socioeconomic Differentials of Undernutrition 

Urban- rural residence 

All findings show higher levels of undernutrition among rural women than urban women, 

although there were declines among both groups between 2000 and 2005. The undernutrition 

level declined from 32.3% to 28.8% among rural women and from 23.2% to 18.9% among urban 

women over the two survey years (Table 3). The decline was more pronounced among women in 

urban areas. 

Region of residence 

As evident from Figure 5 and Table 4, region of residence was an important factor that 

significantly affects the level of undernutrition in Ethiopia. In all of the regions except Tigray the 

level of undernutrition showed a decrease from 2000 to 2005. In Tigray, Afar, Somali, Gambella 

and Ben-Gumuz the undernutrition level was more than 35%. In 2000 the level of undernutrition 

was highest (48.6%) in Somali, though it declined to 36.5% in 2005. In 2005 the level of 

undernutrition was highest in Tigray, at 38.0%, up from 34.9% in 2000. 
16

Multivariate Analysis: Relative Risks of Undernutrition 

Table 4 depicts the adjusted odds ratios of undernutrition (BMI <18.5) by background 

characteristics for rural and urban areas and the total sample, to identify factors affecting the 

level of undernutrition and examine changes over the two survey years. The results show that the 

factors significantly affecting women’s undernutrition changed from 2000 to 2005. In the 2000 

EDHS, age, marital status, religion, occupation, wealth index and region of residence were found 

to significantly affect chronic energy deficiency for the total sample of women. In 2005, 

however, religion and region of residence were no longer determinant factors for women’s 

undernutrition. 

Women age 15-19 and 40-49 were more than 1.5 times more likely (OR= 1.7, 1.7 in 2000 

and OR=1.8, 1.4 in 2005) to suffer from undernutrition compared with women age 20-29. As 

expected, women in the rich category were less likely (OR= 0.74, 0.68) to be affected by 

undernutrition than their non-rich counterparts. Women in Tigray, Afar, Somali, Ben–Gumuz 

and Gambella were more likely to be affected by chronic undernutrition than women in Oromia 

region. 

Tigray 

34.9/38.0

Afar

41.8/32.2

Amhara 

31.9/27.6

Somali 

48.6/36.5 

SNNP

31.2/27.1

Ben-Gum

39.4/35.3

Gambella

38.2/36.8

Addis Ababa 

17.7/15.4

Oromia

29.0/24.7

Harari

23.5/18.8

Dire Dawa 

27.1/23.3

Undernutrition by regions BMI<18.5 

Ethiopia 

30.5/26.9

Figure 5: Percentage of women age 15-49 with CED by region of residence in Ethiopia: 

EDHS 2000 and 2005 
17

Table 4: Adjusted odds ratios of undernutrition (BMI<18.5) among women 15-49 by background 

characteristics in Ethiopia: urban, rural and total, EDHS 2000 and 2005

Background Characteristics 

2000 2005

Urban Rural Total Urban Rural Total 

Age

15-19 1.10 1.92*** 1.70*** 0.96 2.00*** 1.72***

20-29 R

1.00 1.00 1.00 1.00 1.00 1.00

30-39 1.07 1.25** 1.21* 0.94 1.18 1.14

40-49 1.42 1.88*** 1.80*** 0.48 1.64*** 1.42**

Parity

0 0.87 0.85 0.87 0.69 1.02 0.97

1-2

R

1.00 1.00 1.00 1.00 1.00 1.00

3-4 1.03 1.12 1.09 0.68 1.00 0.92

5+ 1.14 0.94 0.94 1.78 1.18 1.18

Marital Status

Never married 1.56* 1.63*** 1.58*** 2.7** 1.02 1.17

Married/Living together R

1.00 1.00 1.00 1.00 1.00 1.00

Widowed/Divorced/Separated 1.44* 0.98 1.08 2.22** 1.08 1.18

Education

No education 1.26 0.94 1.01 1.16 0.90 0.94

Primary education R

1.00 1.00 1.00 1.00 1.00 1.00

Secondary and above 1.45** 0.93 0.96 0.98 0.71 0.82

Religion

Orthodox R

1.00 1.00 1.00 1.00 1.00 1.00

Muslim 1.09 1.16 1.17* 0.67 1.24 1.16

Protestant 1.01 0.91 0.95 0.98 1.04 1.01

Others 1.26 1.13 1.14 0.99 1.01 0.98

Occupation

Agricultural/Manual(skilled/unskilled)R1.00 1.00 1.00 1.00 1.00 1.00

Non-manual/Professional 0.65 0.82* 0.81** 0.84 0.91 0.92

Unemployed/Missing/Don't know 0.93 1.26*** 1.21** 0.92 1.19* 1.15

Wealth Index

Non-richR1.00 1.00 1.00 1.00 1.00 1.00

rich 0.61* 0.90 0.74*** 0.37*** 0.80 0.68***

Region

Tigray 2.06** 1.48*** 1.54*** 1.92* 2.08*** 2.02***

Afar 0.91 2.14*** 1.84*** 0.64 1.62** 1.44*

Amhara 1.71** 1.27** 1.29** 0.78 1.27 1.21

Oromia R

1.00 1.00 1.00 1.00 1.00 1.00

Somali 4.03*** 1.80*** 2.19*** 1.31 1.70** 1.61**

Ben-Gumuz 3.08*** 1.60*** 1.72*** 2.26 1.52** 1.59**

SNNPR 0.88 1.23* 1.21* 0.68 1.15 1.12

Gambella 1.89 2.09*** 1.98*** 1.18 2.18*** 2.053***

Harari 1.00 1.17 0.99 0.97 1.39 1.08

Addis Ababa 0.88 NA 0.70*** 0.78 1.11 0.87

Dire Dawa 1.35 1.55** 1.18 1.25 1.36 1.21

R: Reference Category

Note: NA = Not applicable; *** significant at p<0.01; ** significant at p<0.05; * significant at p<0.1; unmarked = not 

significant
18

Factors Affecting Women’s Undernutrition in Rural Areas 

The level of undernutrition in rural areas was significantly affected by age, marital status, 

occupation and region of residence, but in 2005 marital status was no longer a determinant 

factor. Rural women age 15-19 and 40-49 were more than 1.5 times more likely to be affected by 

chronic energy deficiency than women age 20-29. Never-married women were 1.6 times more 

likely to be affected by chronic energy deficiency than married women. However, in 2005 

marital status was no longer a predicting factor for undernutrition. Unemployed women were 

more likely (OR=1.3, 1.2) to be affected than those working in agriculture, but women in nonmanual/professional jobs were less likely to be affected by chronic undernutrition. Women living 

in five of the regions (Tigray, Afar, Somali, Ben-Gumuz and Gambella) were more than 1.5 

times more likely to be affected by chronic undernutrition than women in Oromia region. 

Factors Affecting Women’s Undernutrition in Urban Areas 

The results of table 4 also depict that in urban areas marital status, education (in 2000 

only), wealth and region of residence significantly affected women’s level of undernutrition. 

Never-married women and widowed/divorced/separated women were more likely (OR=1.6, 2.7) 

and (OR= 1.4, 2.2) to be affected by the chronic energy deficiency than women who were 

married or living together. In the year 2000, urban women with secondary/higher education were 

1.5 times more likely to be affected by chronic undernutrition than women with only a primary 

education. Unlike rural women, for urban women wealth - was significantly associated with 

chronic undernutrition: rich women were less likely (OR=0.6, 0.4) to be affected by chronic 

undernutrition than their non-rich counterparts. Residence in Tigray, Amhara, Somali and BenGumuz regions significantly affected women’s level of undernutrition in 2000 but in 2005 only 

in Tigray did region of residence significantly affect women’s nutritional status. Women living 

in Tigray region were almost twice as likely to be affected by chronic energy deficiency than 

women in Oromia region, over both survey years. 
19

DISCUSSION 

Undernutrition is assumed to be affected by both health and food security status of the 

individual. Thus the evaluation of undernutrition needs to be seen in light of these two pillars. To 

demonstrate the rural-urban disparity in women’s nutritional status for all characteristics, the 

study examined data for rural, urban and total samples over the two survey years, 2000 and 2005. 

This was to better explain the discrepancy between rural and urban women in terms of their ways 

of living. In the rural parts of Ethiopia, less expanded educational institutions, traditional ways of 

farming as only means of surviving, early marriage, less developed infrastructures, and cultural 

and religious influences are assumed to have substantial influence on women’s nutritional status. 

Results of both bivariate and multivariate analysis confirmed that there is a 30% disparity 

in the levels of undernutrition between urban and rural women. Women living in rural areas of 

the country are much more undernourished than their urban counterparts, for both 2000 and 

2005. Similar results have been reported from earlier studies in Ethiopia and elsewhere in subSaharan Africa and in India (Uthman and Aremo, 2008; Ramesh, 2004; Teller and Yimer, 2000). 

The disparity may be due to better infrastructural facilities and higher expenditures on health in 

urban areas. Greater access to health care, safe water and sanitation facilities may be the causal 

factors for better nutritional status among urban women. Moreover, better infrastructure, which 

has a direct influence on food security, is relatively poor in the rural parts of Ethiopia. Another 

important reason associated with low nutritional status of rural women may be the higher levels 

of labor or workload among rural women compared with their urban counterparts. The above 

basic problems in collaboration with slow expansion of education and social services and roads 

in rural parts of Ethiopia may contribute to lower nutritional status among women in rural areas. 

Regarding the regional variations in undernourishment, both bivariate and multivariate 

results for the total sample of women (both rural and urban) confirmed that women who live in 

Tigray, Afar, Somali, Ben-Gumuz and Gambella are at a higher risk of undernutrition than those 

in Oromia region. The multivariate results also showed similar findings: women living in Tigray, 

Somali, Ben-Gumuz and Gambella are at a higher risk of undernutrition than those living in 

Oromia region. 
20

All of the regions having a relatively higher prevalence of undernutrition are found in the 

lowlands of the country, with the exception of the highlands of Tigray, where undernutrition is 

also prevalent. Overall, women living in the lowlands are more undernourished than women 

living in the highlands (Jemal et al., 2003; Woldemariam and Timotiwos, 2002). The probable 

reason for this could be associated with livestock holding (more in the lowlands), land ownership 

(greater in the lowlands), education (less literacy in the lowlands) and types of illness (more 

malaria in the lowlands). A cultural problem associated with pastoral communities that limits 

their required dietary intake from crop products could also be a reason for high prevalence of 

undernutrition in the lowlands of the country. Moreover, the educational level of women in the 

lowlands was significantly below that of women in the highlands. It can also be assumed that the 

low level of educational background could be associated with women’s lack of decision-making 

power, which in turn has played a role in increasing the undernutrition rate in the lowlands of 

Ethiopia (Jemal et al., 2003). In addition, tropical diseases like malaria and sleeping sickness are 

relatively more common in the lowlands of Ethiopia, which are immediate factors that reduce the 

nutritional status of women. 

The low BMI value in women living in the highlands of Tigray may be related to low 

agricultural productivity because of shortage of arable land and livestock products. The low 

nutritional status of women found in Tigray and Amhara regions might be associated with land 

degradation, land fragmentation and protracted war frequently occurring in the region (in 

Tigray). It is also known that Tigray and Amhara regions (the northern parts of the country) have 

been the center of agriculture since the dawn of civilization. Thus fertility of the soil has been 

overused (degraded) over many years to the degree that it cannot support the current high 

population in the area. As a result, many people and especially farmers in rural areas are living 

from subsistence agriculture, which may be a coping strategy. 

The protracted war in Tigray has also had an impact on food security by hampering 

people’s access to food supplies. In addition, the recent land redistribution in Amhara and Tigray 

has created fragmentation of the land, which in turn affects the production per head, and at the 

time of the study people may not have been encouraged to use key technological inputs in 

agriculture (fertilizers, pesticides). 
21

Our study found that young women (age 15-19) and older women (age 40-49) are more 

likely to be undernourished than women age 20-29. The result of the multivariate analysis also 

confirms that never-married women age 15-19 and 40-49 are significantly more affected by 

undernutrition than women age 20-29, but age is not a factor that significantly affects urban 

women at all levels. Other studies have found similar results (Teller et al., 2000; Woldemaraim 

et al., 2002). The fact that unmarried adolescent women are often at the bottom of the food chain, 

with little or no decision-making power in the household about food distribution, could lead to 

food security issues and may contribute to their poor nutritional status. Moreover, women age 

15-19 need adequate nutrients to support fast physical, mental and emotional growth. 

Unawareness of adolescent women about their own health and nutritional status could be another 

reason associated with their poor nutritional status. In addition, lower nutritional status among 

rural women could be explained partly by the fact that rural women age 15-19 are more 

vulnerable to early marriage and childbearing than women in urban parts of the country (Central 

Statistical Authority [Ethiopia] and ORC Macro, 2006). Hence, in addition to their own health 

needs, rural woman need adequate dietary intake for child growth and pregnancy. 

Rural women age 40-49 are relatively less educated than younger women and therefore 

may have low decision-making power, which inhibits their control over income and household 

assets. Moreover, older women have a relatively higher-level parity (more children ever born), 

which could obligate them to take care of their children rather than protecting their own health 

and nutritional status, given limited household resources. 

In urban areas never-married women are more likely to be undernourished than women 

who are married or living together. However, marriage has no effect on undernourishment status 

in rural areas. Due to cultural values and poor decision-making autonomy, women have little 

access to higher education and high- and middle-income jobs. Hence, many women are 

dependent on their partners and thus may not be able to get adequate nutrition if they have no 

partner. In addition, in most cases never-married women belong to households with relatively 

poor economic status, which may also make it more difficult to obtain sufficient food. 

In rural areas, occupation was found to be one of the determinant predictors of risk of 

undernutrition. Unemployed women are more likely, and non-manual and professional women 

are less likely, to be undernourished than agricultural/skilled/unskilled women. The same result 
22

has been observed among other sub-Saharan countries (Hindin, 2000 and 2005). The probable 

reason for this could be that women who work in agriculture have lower educational status than 

women working in non-agricultural jobs (EDHS, 2005), and they have relatively less decisionmaking autonomy and less control over income than women with non-agricultural jobs. 

Another important determinant of the nutritional status of women in Ethiopia is 

household wealth. As expected, both bivariate and multivariate results, particularly for urban 

women, confirmed that those with non-rich wealth status are at a higher risk of undernutrition 

than their rich counterparts. This finding is also consistent with other studies (Edilberto, 1997; 

Teller et al., 2000; Woldemaraim et al., 2002). This may indicate that household economic status 

has a direct association with household food security. Household food security in turn is a 

precondition for daily dietary intake for all household members. 

Another important finding that influences women’s nutritional status is partner’s 

characteristics (education and occupation). The results of the bivariate analysis illustrate that, 

with increasing partner’s educational levels from no education to secondary and above, the level 

of undernutrition among women shows a linear decline in both rural and urban areas and for both 

survey years. Similarly, the level of undernutrition across occupational status declines as 

partner’s occupational status rises. The same finding has been observed in other sub-Saharan 

countries (Hindin, 2005). The probable explanation for this could be that women with 

agricultural/skilled/unskilled or unemployed partners often have lower educational levels and 

less autonomy than those whose partners have higher occupational status. In developing 

countries like Ethiopia, where women are highly dependent on their partners, women with lowstatus partners (in terms of educational and occupational status) have less access to household 

food security, which increases the rate of undernutrition. 
23

CONCLUSION AND POLICY RECOMMENDATIONS 

Our study found that in Ethiopia rural women were more likely to be affected by 

undernutrition than their urban counterparts. Women age 15-19 and 40-49 were more likely to be 

undernourished. Never-married women were more likely than other women to be affected by 

undernutrition, particularly in urban areas. In urban areas rich women were less likely to be 

undernourished, but wealth had no effect on undernourishment in rural areas. Among regions, 

women living in Tigray (both rural and urban), Afar (rural), Somali (rural) and Gambella (rural) 

were highly likely to be undernourished. Unemployed women were more likely to be 

undernourished, particularly in rural parts of the country. Regarding partner’s status (education 

and occupation), women whose partners had lower educational and occupational status were 

more affected by undernutrition than those whose partners had higher status. 

Considering these important findings, the following policy recommendations are 

suggested: 1) disseminate the findings of the study in Ethiopia; 2) increase agricultural 

productivity particularly in Tigray, Afar, Gambella, Ben-Gumuz and Somali regions; 3) promote 

health service provision systems in these regions; 4) expand women’s education, particularly in 

rural areas; 5) create employment opportunities, particularly in urban areas. 
24

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