Research Thesis defence ppt_Anteneh!.pptx

Determinants of Poverty in Urban Households
(The Case of Aysiata District in Afar Region, Ethiopia)
By
Anteneh Tadele

Determinants of Poverty in Urban Households
A research thesis submitted to the college of Business and Economics department
in partial fulfilment of the requirements for the Degree of Master of Science in
Developmental Economics
MajorAdvisor: MohammedAdem (Asist. Prof)
Co – advisor: Abdulrezak Nejmuhdin (MSc)
February 2024
Samara University, Ethiopia

Outline
1. Background of the Study
2. Statement of the Problem
3. General and Specific Objective, Research Questions
4. Research Methodology [ Data Type, Sources, Analysis]
5. Result and Discussion
6. Summary and Conclusion
7. Recommendation

1. Background of the study
 The study aimed to describe the determinants of poverty urban household in Aysaita district in
Afar region, Ethiopia. The focus was how households can improve income to out of poverty line
through successful knowledge-sharing using CBN approach.
 Poverty in the simplest sense of the word is a state where one lack of accessing to basic needs
such as food, clothing, and shelter. Poverty is defined access to services and security critical to
well-being and not just income and consumption (WV, 2022).
According to the World Bank 2000s, 2015, and 2021, the poverty line per day is $1, $1.90, and
$2.15/day, respactivily but currently, 689 million people were living in extreme monetary poverty
in 2017, WB, 2020
Ethiopia is one of the poorest countries in the world with a large portion of its population believed
to be living below poverty line. As shown from WB, 2021 and WFP, 2023 result, the headcount
index of absolute poverty of Ethiopia was decreased from 44.2 percent in 2000 to 23.5 percent, in
2015 and 27.8 percent in 2016, respectively.

Cont.…
Afar region is one of the poorest and least developed regions of Ethiopia, neglected by
national development efforts. According to regional report by (BoFED 2020), food
security situation in Aysaita district was below normal.
MoFED, 2019 report shows that, the headcount poverty incidence in Afar region
declined from 41.9 percent in 2004/05 to 32.8 percent in 2020/21. In the same period,
the rural poverty head count index declined from 45.2 percent to 35.1 percent while
urban declined from 35.3 percent to 23.1 percent.
According to DRMFSAB, 2020 report, poverty situation in Aysaita district was high,
the zone was suffering food related problems.
Therefore, to assist the poor in the district it requires identifying the factors determining
poverty in locally specified context and need to measure the intensity of poverty in the
district.

2. Statement of the Problem
 Conducting a research on determinant of poverty is crucial for identifying the level
of income and expenditure to establish a sustainable competitive usage and improve
overall profitability of the household incomes. The conclusion that poor households is
positivity correlated with their explanatory variables has both proponents and
opponents in the empirical literatures.
 Bashir (2010) and Sharma (2014) shows that the age of household head is negatively
correlated with the probability of being poor. Whereas Indris (2012) found out that
age of the household head affects food insecurity positively.
 Abubeker M/d et al 2014, show that in the rural site of the study area, 52.8% have
been living below the poverty line with poverty gap and poverty severity indices of
0.16 and 0.07, respectively and Gini coefficient of 0.31.

Cont.…
 Most research studied on poverty is target individual rather than household level.
On the other hand, study not to conduct that much to show the impact of
determinants of unidimensional poverty using CBN approach in the district.
There is an insignificant number of research works focusing on poverty using
unidimensional methods in the Aysaita district – especially in urban area.
 Adding to this, some variables that can determine poverty are included like
Children under 14 years and ownership of cell phone service were used to
differentiate others researcher like (Abubeker M/d, Ayalneh Bogale, and Asefa
Seyoum 2013/14) conducted in Aysaita in rural area.
 More Quantitative methods of data collection were employed, and under
Unidimensional than Multidimensional poverty type – CBN approach was used for
setting poverty line.

3. Objectives and Research Questions
 General Objective
To identify the determinants of poverty status of urban households in the case of
Aysaita District of the Afar Regional State.
 Specific Objective
1. Identify the poverty status – incident of households.
2. Estimate the level and intensity of poverty of households.
3. Analyze factors affecting poverty status of households.
 Research Questions
1. What is the poverty status of the respondents?
2. What are the levels of poverty among households?
3. What are the determinants of poverty among households?

4. Methodology of the research
 The researcher used Primary and some Secondary source of data and more used
for Quantitative data than qualitative using structural questionnaire and its sampling
procedure was multi-stage sampling.
 Finally, using Yemane’s (1971) sample size formula, 288 sample households were
selected randomly based on Probability Proportional to Size (PPS) of the population
in each kebele.
 Data analysis used for cross-sectional data from 288 sample and, under
Unidimensional poverty type – CBN approach were employed to identify poverty
line using Foster-Greer-Thorbecke (FGT) index for descriptive and,
Under Limited Dependent Variable Model [Because of the DV is binary 0/1]
logit model were used for econometrics analysis to identify determinant of poverty.

Model Specifications
Limited Dependent Variables model of magnitude/intensity, and determinant of poverty.
Structure of the models: E(yi/x) = Pr[yi = 1/x] = G[β1 + β2X2i+ β3X3i …….+ βkXki]
Identify Poor households – CBN approach, FGT equation y1𝑖= 𝑋1𝑖𝛽1+𝜀1……...(1)
y1𝑖 = 1 if y1𝑖 is poor
0 otherwise
2. Determinant of poverty - logit & Marginal effect y2𝑖= 𝑋2𝑖𝛽2+𝜀2𝑖……. (2)
Where; y2i is observed if and only if y1𝑖  1 [poor]; the variance 𝜀2𝑖 is normalized to one because
only y2𝑖 not y1i .
DV: Poverty incidence – which a dummy variable [1- Poor; 0 – non-poor]
IV: Sex, Age, Marital status, Edu_level, FamSz_AE, Children under 14, Emp_level, TLU_AE,
Own Cellphone, Credit services, Marketplace access and Market Distance.

5. Result and Discussion
 As we know, unidimensional poverty measurement involves two steps (Sen 1976):
Identification - to identify who is poor and, Aggregation - effects how poor the respondents is?.
 Descriptive Analysis
 Analysis of Discrete Variable
Variable Mean Std. Dev Min Max
Age 39.12 10.91 22 69
FamSZ_AE 3.9 1.8 2 9
Child<14 1.8 1.1 0 4
Child>51 0.24 0.52 0 2
TLU_AE 8.43 3.90 0 18
Percapinc 1545.97 830.82 277.78 4756.50
Mark_Dist 3.54 1.38 1 7

Cont.…
 Analysis of Categorical Variable
Variable Category Frequency Percent
Name of Kebele
Amolederewa(04) 76 26.39
Kulsi’coma(02) 67 23.26
Beri’daba (01) 86 29.86
Aberoberi’fagi (03) 59 20.49
Sex of the household head Female 93 32.29
Male 195 67.71
Marital status of household head Unmarried 64 22.22
Married 232 77.78
Educational status of household
head
Illiterate 89 30.90
Primary Education 112 38.89
Secondary Education 65 22.57
Higher Education 22 7.64

Conti…
Variable Category Frequency Percent
Employment Status of household
head
Unemployed 198 68.75
Employed 90 31.25
Households Having Agricultural
area
No 87 30.21
Yes 201 69.79
Households Having Livestock
Owen
No 43 14.93
Yes 245 85.07
Household accessing Market
service
No 133 46.18
Yes 155 53.82
Households accessing Credit/Loan
Service
No 227 78.82
Yes 61 21.18
Household having own Telcom or
Cellphone
No 167 57.99
Yes 121 42.01

Cont.…
Sources of Income of the Household heads
 Based on the nature of their livelihoods, households in the district had depend on different
sources of income. We found that households participate in a range of types of employment or
activities to generate income and maintain themselves.
 From the respondent household survey result shows that, 38.69 percent of the households do not
have extra jobs to improve their monthly or yearly income.
Sources of Income Frequency Percent
Government employed [recruited] 69 23.96
On-farm (Agricultural product) 49 17.01
Off – farm (Livestock products) 53 18.40
Self-employed: petty trade, charcoal sold 81 28.13
Private Institution employed: 36 12.50

Dimensions of Poverty among Urban Households
Status of Poverty - Who are the Poor?
Using CBN approach, the following steps were employed to obtain the food poverty line.
i. Identify and select 11 food items commonly consumed by most of the poor.
ii. Each food item in the bundle of goods is weighed in kilograms and liters.
iii.Each unit of the food items is divided by the adult equivalent units-AEU and sum all.
iv. 2,200 kcal being the minimum calorie required adult equivalent per day in Ethiopia
MoFED, 2020.
Finally, a given food expense value was a poverty line threshold that provides a
monetary value for the food and non-food component, so the food expense value
was 2,823ETB and from this 23 percent [649.29 ETB] are use the non-food poverty
which the share of the lowest expenditure distribution so food poverty line was 3,472
ETB household per month but per adult equivalent is [3,472/4] = 868 birr.

Cont..
s/n Food items kcal/100g Kg/month
/AEU
Kcal/month
/AEU
Kcal/AEU
/Day
Share
(%)
Average
price
Food
expenses
1 Teff 358 10.10 25,310.60 844 0.19 80.0 808
2 Wheat 326 4.3 9,812.60 327 0.11 65.0 280
3 Maize 361 5.2 13,140.40 438 0.20 55.0 286
4 Sorghum 344 1.75 4,214.00 140 0.06 60.0 105
5 Onion 37 2.15 556.85 19 0.01 80.0 172
6 Red paper 311 0.7 1,523.90 51 0.02 180.0 126
7 Meat 289 1.5 3,036.08 101 0.05 450.0 675
8 Sugar &Salt 400 0.5 1,400.00 47 0.02 80.0 40
9 Milk 65 1.21 550.55 18 0.01 80.0 97
10 Oil 900 0.5 3,150.00 105 0.05 350.0 175
11 Coffee &tea 1180 0.4 3,304.00 110 0.05 150.0 60
Total 4,571 28.31 65,998.98 2,200 77% 2,823

Dimensions of Poverty among Urban Households
 FGT index estimates, FGT(a) and Gini Coefficient
Explanatory
Variables Category
Head Count
(P0)
Gap index
(P1)
Severity
(P2)
Gini
Coefficient
Overall index 0.382 0.104 0.032 0.232
Name of Kebele
Amolederewa (04) 0.355 0.101 0.032 0.245
Kulsi’coma (02) 0.418 0.117 0.037 0.216
Beri’daba (01) 0.349 0.098 0.031 0.229
Aberoberi’fagi (03) 0.424 0.102 0.028 0.228
Gender of
Household Head
Female 0.484 0.137 0.044 0.248
Male 0.333 0.088 0.027 0.222
Marital status of
Household Head
Unmarried 0.672 0.189 0.061 0.229
Married 0.299 0.079 0.024 0.224

Cont..
Explanatory
Variables
Category P0 P1 P2 Gini
Overall index 0.382 0.104 0.032 0.232
Educational Status
of Household Head
Illiterate 0.736 0.739 0.740 0.156
Primary Education 0.154 0.143 0.133 0.208
Secondary Edu 0.100 0.112 0.122 0.222
Higher Education 0.136 0.045 0.016 0.145
Employment Status
or level
Unemployed 0.471 0.130 0.041 0.227
Employed 0.159 0.038 0.010 0.226
Accessing
Market center
No 0.551 0.153 0.048 0.229
Yes 0.227 0.059 0.017 0.216
Accessing Credit
or loan Service
No 0.440 0.119 0.037 0.235
Yes 0.129 0.036 0.012 0.199
Cellphone
Ownership
No 0.471 0.134 0.042 0.234
Yes 0.222 0.060 0.019 0.217

Status of Income Inequality in District - Lorenz Curve
Gini index value, 01 kebele = 0.229, 02
kebele = 0.216, 03 kebele = 0.228, and 04
kebele = 0.245
In the study area, the overall income
inequality was 0.232.
Results show that the Gini coefficient of 04
kebele is 0.245 while 01 is 0.229 and 03
kebeles is 0.228, therefore, 04 kebele was
slightly higher .
 The relatively high Gini coefficient is in 02
kebele which is 0.216 indicates that unequal
distribution of consumption expenditure.
 Therefore, the closer the line of equality the
lower the income inequality.
0
10
20
30
40
50
60
70
80
90
100
0 20 40 60 80 100
%
of
Cuml
Income
% of population
Inequality Curve
03 kebele
04 kebele
01 Kebele
02 Kebele

Econometric Analysis
 Binary logistic regression analysis was used to identify the effect of each
independent variable on the studied district poverty status of the households.
Before, regress the logistic model, check the goodness of fit in the predicted
variables using the Hosmer-Lemeshow test model. Therefore,
Logistic model for poor, goodness-of-fit test
number of observations = 288
number of groups = 10
Hosmer-Lemeshow chi2(8) = 9.40
Prob > chi2 = 0.3094
 The results indicates there is a good logistic regression model because of there
is a small chi square with large p-value greater than 0.05. Therefore, there is NO
difference between the observed and the model predicted values.

Econometric Analysis result
 Since the logistic model is nonlinear, the marginal effects of each independent variable
on the dependent variable are not constant but they are dependent on the values of the
independent variables (Green, 1993, cited in J.G. Rodriguez, 2010).
 The results of the logistic model were given below consisting of the variables, the
estimated odds ratio, and the marginal effects for explanatory variables included in the
model. The odds are the ratio of the probability of being poor to the probability of not
being poor.
 The odds ratio gives the change in the odds of being poor as opposed to not being
poor. It describes the correlation between the dependent and independent variables, i.e.,
Positively or Negatively correlated of the probability of being poor to non-poor.
The Marginal effect is the percentage change in probability associated with a unit
change in the explanatory variable.

Logistic & Marginal effect output
Logistic regression Number of obs.
= 288
LR chi2(15) =
274.27
Prob > chi2 =
0.0000
Log likelihood = -54.385646 Pseudo R2 =
0.7160
Independent Variables Odds Ratio Std. Err. Z P>|z| Marg. Effect
Sex of hh head (1.
Male)
.878 .482 -.24 0.813 -.0074
Age of hh head 1.709 .346 2.64 0.006** .0304
Age_sqr .995 .002 -2.41 0.013** -.0003
Martl_stat .925 .0703 -3.13 0.002** -.1546

Cont.…
Independent Variables Odds Ratio Std. Err. Z P>|z| Marg. Effect
Education .286 .151 -2.36 0.013* -.0710
Illiterate 1.128 .859 0.16 0.875 .0068
Emply_sta (1.
Employed)
.728 .493 -0.47 0.642 -.1832
TLU_AE .715 .061 -3.95 0.000*** -.0190
Percapinc .999 .0001 -5.29 0.000*** -.0015
Mkt_access (1. Yes) .275 .161 -2.21 0.027** -.0771
Credit_serv (1.
Yes)
.412 .368 -0.99 0.326 -.0511
Own_phone (1.Yes) .797 .449 -0.40 0.689 -.0129

6. Conclusion
 Findings of this study shows that on average, the age of the respondent was 39 years and 32.29
percent of the respondent are female headed and out the household heads, 22.22 percent were
unmarried. The average per capital income per family size was 1,545.97ETB.
The results show that the overall incident of the poor household in the district was 38.2 percent
with a poverty gap of 0.104, poverty severity of 0.032 and Gini coefficient or level of inequality
across the district was 0.232.
 Out of the total 13 explanatory variables, 7 of them are significant at 1 and 5 percent of
statistical level and 3 of them are positively correlated with the poverty likelihood of being poor
households. So, statistically significant explanatory variables are Age, Agesqr, FamSZ_A,
Martl_stat, Education, TLU_AE, Percapinc, and Mkt_access.
 The likelihood ratio Chi-square of 274.27 with a p-value of 0.0000 tells us that our model fits
significantly better than an empty model (i.e., a model with no predictors).

7. Recommendation
The five most important sources of vulnerability in the district were found, this are food
price/inflation, drought, increasing temperature, lack of agricultural input, and human and
livestock disease. So, to reduced this vulnerable, the following are the recommended methods.
 Increasing livestock and agricultural production as well as accessing market linkage.
 Education is a weapon one must alleviate poverty so expanding schooling access is a vital for
reducing the vulnerability of household in urban poverty. Besides, reducing unemployment
through job creation, it can provide technical assistance for self-employed opportunity for the
household members.
 Households in obtaining financial services reduces urban poverty. Allowing urban residents to
receive credit services will provide financial freedom for household members to launch new
economic operations, reducing the many facets of poverty.
 Improving access to major services and facilities including market access and credit services
should remain the major focus of development interventions by the district authorities.

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