research

1. i
DETERMINANTS OF URBAN POVERTY: A
HOUSEHOLD LEVEL ANALYSIS IN CASE OF DEBRE
BIRHAN TOWN, ETHIOPIA.
DEPARTMENT OF ECONOMICS
COLLEGE OF BUSINES AND ECONOMICS
DEBRE BIRHAN UNIVERSTY
BY:
MULUGETA SHIFERAW
MAY, 2019
DEBRE BIRHAN, ETHIOPIA

2. ii
DETERMINANTS OF URBAN POVERTY: A HOUSEHOLD
LEVEL ANALYSIS IN CASE OF DEBRE BIRHAN TOWN,
ETHIOPIA
DEPARTMENT OF ECONOMICS
COLLEGE OF BUSINES AND ECONOMICS
DEBRE BIRHAN UNIVERSTY
BY:
MULUGETA SHIFERAW
ADVISOR:
KURABACHEW MENBER (PHD)
A Thesis Submitted to the Department of Economics of Debre Birhan University for the
Partial Fulfillment of Masters of Science in Economics (Development Economics).
MAY, 2019
DEBRE BIRHAN, ETHIOPIA

3. iii
APPROVAL
As members of Board examiners of the final MSc. Thesis open defense examination, we certify
that we have read and evaluated the thesis prepared by Mulugeta Shiferaw entitled
―Determinants of urban poverty in case of Debre Birhan town‖ and examined the candidate. We
recommend that thesis be accepted as fulfilling the thesis requirement for the degree of masters
of Science in Economics (Development Economics).
Board of Examiners
External Examiner ________________________ ____________________
Internal Examiner _________________________ _____________________
Chair person _______________________ ______________________
Date: _____________________

4. i
DECLARATION
I, the undersigned, declare that this thesis is my own original work and has not been presented in
any other university. All sources of materials used for this thesis have been duly acknowledged.
Name: Mulugeta Shiferaw
Signature: ---------------------
Date: ---------------------

5. ii
CERTIFICATE
As Thesis Research advisor, I hereby certify that I have read and evaluated this thesis prepared,
Under my guidance, by Mulugeta Shiferaw , entitled ―Determinants of urban poverty in case of
Debre Birhan town ‖. I recommended that it be submitted as fulfilling the thesis requirement for
the degree of masters of Science in Economics (Development Economics).
Kurabachew Menber (PHD) 28/05/2019
Name Signature Date

6. iii
ACKNOWLEDGEMENTS
First and foremost, I would like to express my gratitude to God for his abundant grace that I am
able to be what I am today. I also wish to express my deepest gratitude to my advisor; DR.
Kurabachew Menber for his unreserved support and encouragement from the establishment up to
the accomplishment of this paper. Thank you for his valuable insight. I am also would like to say
thanks for my boss and my colleagues for their assistance of useful information. I would like to
gratitude to Debre Birhan city service and all respondents for their cooperation to provide
relevant information. Finally, I deeply wish to pay highest gratitude to my parents for their
support, encouragement and advice.

7. iv
Contents
ABBREVIATIONS.......................................................................................................................... ix
ABSTRACT ......................................................................................................................................x
CHAPTER ONE
1. INTRODUCTION .......................................................................................................................1
1.1.Background of the study........................................................................................................1
1.2.statement of problem..............................................................................................................4
1.3. Objective of the study............................................................................................................7
1.4. Significance of the Study ......................................................................................................7
1.5. Scope of the study .................................................................................................................7
1.6. Limitation of the study .................................................................................................................... 8
1.7. Organization of the Paper................................................................................................................. 8
1.8. Hypothesis ...................................................................................................................................... 8
CHAPTER TWO
2. LITERATURE REVIEW
2.1. Theoretical literature review .......................................................................................................... 11
2.1.1. Concept and definition of poverty............................................................................................... 11
2.1.2. Context of urban poverty ............................................................................................................ 14

8. v
2.1.3. Determinants of poverty.............................................................................................................. 15
2.1.4. Cause of poverty ......................................................................................................................... 16
2.1.5. The difference between absolute and relative poverty ................................................................ 17
2.2. Empirical literature review ............................................................................................................ 18
2.2.1. Poverty in Ethiopia...................................................................................................................... 18
2.2.2. Identifying poverty line .............................................................................................................. 19
2.2.3 Main indicators used to measure poverty ..................................................................................... 21
CHAPTER THREE
3. RESEARCH METHODOLOGY ...................................................................................................... 24
3.1. Description of the Study Area ....................................................................................................... 24
3.2. Data types and sources .................................................................................................................. 26
3.3. Size and Sampling Technique ....................................................................................................... 27
3.4. Data collection methods and procedures ....................................................................................... 28
3.5. Method of data analysis ................................................................................................................ 29
3.5.1. Descriptive analysis ................................................................................................................... 29
3.5.2. Econometric analysis ................................................................................................................. 29
3.5.2.1. Model specification ................................................................................................................. 29
3.5.2.2 Description of Variables ........................................................................................................... 31

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CHAPTER FOUR
4. DATA ANALYSIS, PRESENTATION AND DISCUSSION OF THE RESULTS
4.1. Identifying the Poor ...................................................................................................................... 32
4.2. Descriptive Analysis, demographic Characteristics of sampled households ................................ 36
4.2.1. Sex and urban poverty................................................................................................................. 37
4.2.2. Age and urban poverty................................................................................................................ 38
4.2.3. Marital status and urban poverty................................................................................................. 39
4.2.4. Household family size and urban poverty ................................................................................... 40
4.2.5. Education and urban poverty....................................................................................................... 40
4.2.6. Employment and urban poverty .................................................................................................. 41
4.2.7. Household head income and urban poverty................................................................................. 41
4.2.8. Health status/sick member and urban poverty............................................................................. 42
4.2.9. House, water and electricity ownership and urban poverty ......................................................... 42
4.3. Econometric analysis, Diagnostic test ........................................................................................... 43
4.3.1. Test for multicollinearity............................................................................................................. 44
4.3.2. Test for hetroscedasticity ............................................................................................................ 44
4.3.3. Test for autocorrelation .............................................................................................................. 45
4.3.4. Test for normality of residuals .................................................................................................... 45
4.3.5. Specification link test.................................................................................................................. 45

10. vii
4.3.6. Goodness of fit test ..................................................................................................................... 46
4.4. Econometrics estimation .....................................................................................................46
4.5. Effects of changes in determinant variables .................................................................................. 50
4.5.1 Marginal effect of changes .......................................................................................................... 50
CHAPTER FIVE
5. SUMMARY CONCLUSION AND RECOMMENDATION .......................................................... 54
5.1. Summary and Conclusion ............................................................................................................. 54
5.2. Recommendation .......................................................................................................................... 57
Reference ................................................................................................................................ 60
Questionnaire............................................................................................................................ 66

11. viii
List of Tables
Table 1: Main determinants of poverty .................................................................................................... 15
Table 2: Total sampled household‘s proportion to the total population size from sampled kebeles.......... 28
Table 3: Description of explanatory variables in the logistic model ......................................................... 31
Table 4: Calorie Contents of Different Food Items…………………………………………35
Table 5: Logit estimates the odds of factors affecting urban household poverty....................................... 47
Table 6: Estimation results of binary logit model ..................................................................................... 51
Table 7: Total population in Debre Birhan town....................................................................................... 73
Table 8 : The total Population size in 2016 in each kebeles...................................................................... 73
Table 9: Summary of all dummy variables which describe household Characteristics……..74
Table 10: Summary statistics of continuous explanatory variables by poverty status………75
Table 11: Household head employment and urban poverty ...................................................................... 75
Table 12: Multicollinearity Test (vif) result.............................................................................................. 76
Table 13: Test for Hetroscedasticity ......................................................................................................... 76
Table 14: Test for normality of residuals .................................................................................................. 77
Table 15: Specification link test for single equation model ...................................................................... 77
List of Figures
Figure 1: Map of the study area ................................................................................................................ 26

12. ix
ABBREVIATIONS
ACSI Amhara Credit and Saving Institute
Cal Calorie
CBN Cost of Basic Needs
CSA Central Statistics Authority
EEA Ethiopian Economics Association
EHNRI Ethiopian Health and Nutrition Research Institute
ETB Ethiopian Birr
FDRE Federal Democratic Republic of Ethiopia
FEI Food Energy Intake
HH Household
MoFED Ministry of Finance and Economic Development
NGO Non Governmental Organazation
UNDP United Nation Development Program
USD United States Dollar
WB World Bank

13. x
ABSTRACT
This study was conducted in Debre Birhan town with specific objectives of assessing household
poverty status, and identifying the determinants of urban household poverty. The study made use
of primary data collected from 203 selected sample households by conducting pre prepared
structured questioner employing multi-stage sampling technique. A Logistic regression model
was employed and estimated based on the primary data, with the probability of a household
being poor as a dependent variable, and a set of demographic and socioeconomic variables as
the explanatory variables. By making use of Food Energy Intake (FEI) approach the surveyed
households are identified as the poor and non-poor. Based on this, out of the 203 surveyed
household heads, 48(%) of them were found poor. Econometric results of the binary logit
regression model revealed that; sex, marital status, family size, education, income, health status,
housing and electricity were found to be statistically significant. Hence, market share,
information flow, higher education and health service, good infrastructure , labor absorb
market, opportunities and productive industries are indispensible policy interventions to better
target urban poverty.
Keywords: Debre Birhan ,urban Poverty, household and determinants.

14. 1
CHAPTER ONE
1. INTRODUCTION
1.1. Background
Poverty can only be reduced in the presence of strong institutions, and equitable distribution of
resources. This requires a non-corrupt government. However, in Africa, programs designed to
fight poverty are not fully implemented because the funds end up in the hands of corrupt
individuals, who pocket the majority. Again due to poor governance, those in authority have
failed to apprehend the corrupt. This creates an imbalance in society and leads to more poverty
because it ends up with a few influential and powerful individuals oppressing the poor (who are
the majority). Another leading cause of poverty in Africa is the prevalence of diseases (such as
malaria, HIV/AIDS, TB, EBOLA etc) due partly to inadequate and poor health facilities. When a
household is affected by any of the diseases, the little resources are spent on treating the sick. In
a worst case scenario where the bread winner dies, those who are left behind have no resources
to support themselves, thus leading to poor lifestyles. The loans given out by the World Bank
and IMF have also contributed to the poverty in Africa. Such loans come with strict
conditionality, which usually require governments to adjust some of their economic decisions.
For instance, the requirement to reduce total government spending in African countries has
affected major social sectors such as education, health and infrastructure, which are drivers of
economic development (Alex Addae-Korankye, 2014).
Ethiopia is one of the World‘s poorest countries. Out of a population of around 80 million (2008)
people, 35 million people are living in abject poverty. In one of the world‘s poorest countries,
where about 44 percent of the population lives under the poverty line, more than 12 million
people are chronically or at least periodically food insecure. Most of them live in rural areas with
agriculture as their main occupation. With 80% of Ethiopians dependent on agriculture as their
main livelihood, severe arid conditions due to persistent lack of rainfall coupled with civil
disputes have worsened Ethiopian poverty (World Bank, 2017).

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Elimination of poverty is one of the global challenges that ever faced mankind and provoked
global action exemplified by the millennium development goals. These challenges especially in
developing countries have become a prior problem to attain food security and self- sufficiency
for their citizens. Poverty reduction is possibly the ultimate goal of all development and by
implication development policy focusing attention on the poor could be contributing to both
growth and equity Datt and Ravallion (1992). Poverty disproportionately concentrates in sub-
Saharan Africa (SSA); and SSA is the only part of the world where poverty has increased in
absolute terms.
Ethiopia is among the poorest third world countries in SSA countries with an annual average per
capita income of US$116. Statistics on Ethiopian poverty shows that about 44 percent of the total
populations (45 percent in rural and 37 percent in urban areas) are found to be below poverty
line, caring equivalent to 45 US cents per day (MOFED, 2006). Ethiopia ranks 170/177 in
Human Development Index (World Bank, 2004a). Ethiopia has a total population estimated at
75.1 million in mid July 2006 with a growth rate of 2.62 percent per annum (approximately
additional 2 million people per year) (World Bank, 2004b). The overwhelming majority (84
percent) resides in rural areas where agriculture is a predominant economic activity,
infrastructure and social services are not well developed. Only 16% of the population is urban
dwellers (CSA, 2006).
Poverty has existed for a very long time, and to different extents it remains to be a worldwide
social evil still now in the 21st
century (FAO, 2012). More than two thirds of the 1.4 billion
people who live in extreme poverty reside in rural areas of the developing countries (IFAD,
2011). Poverty in Ethiopia is a longstanding problem affecting a significant portion of its rural
and urban population. Survey results of HICES indicated that the proportion of population below
poverty line in Ethiopia stood at 30.4% in rural areas and 25.7% in urban areas in the 2010 fiscal
year (MOFED, 2012).
Recently, the MPI value of Ethiopia was 0.564 (HDR, 2013). Although there is a declining trend
of poverty both at regional and national levels, the highest food poverty was noted in Amhara
National Regional State with a head count index of 42.5% according to the regional statistical

16. 3
figures of MOFED (2012). Rural and urban poverty head count index in the region stood at
30.7% and 29.2%, respectively in which the former was above the national head count index of
29.6% during the 2010/11 indicating that rural poverty was a widely spread problem in the
region leaving rural households still poor.
Ethiopian urbanization rate (16%) was lower than the sub-Saharan average of 30%. However,
recently due to high rural-urban migrations and population growth of nearly 3.8%, remarkable
urban expansions are observed. If managed proactively, the expansion of urban areas presents a
huge opportunity to shift the structure and location of economic activity from rural agriculture to
the larger and more diversified urban industrial and service sectors. However, poor management
and planning in urban Ethiopia results in rising unemployment, challenges in the provision of
infrastructures, services, and housing. Hence, low quality of life, low life expectancy, food
shortages and high incidence of poverty characterize most of the urban areas (World Bank,
2015).
The multi-dimensional character of poverty in Ethiopia was reflected in many respects, such as
destitution of assets, vulnerability and human development. The government had understood the
multi-dimensional impacts of poverty and put poverty alleviation and reduction as major socio-
economic and political issue in the country. The existence of large number of poor people and
the prevalence of economic inequality may bring about social tensions which would induce
various criminal acts if situations go beyond the limits of social tolerance. Poverty alleviation
would, therefore, enhance economic development and result in improved incomes and better
well-being of the people which is a pre-requisite for peace and further development (Asmamaw
E. 2004).
The relatively higher incidence of urban poverty in the town requires identification of the major
causes of poverty, highly poverty stricken part of the population and where actually most of the
urban poor located in. Doing so will ease the task of policy makers and development partners of
the country to efficiently target poor urban households using the appropriate mode of
interventions (Mohammed, 2017).

17. 4
To have a meaningful intervention and to assist the poor it requires identifying the root causes of
poverty in the urban specified context and need to measure poverty. Analyzing poverty in its
specific context using quantitative and qualitative (participatory poverty assessment) ways
informs and improves the relevance of intervention; in addition it helps to monitor poverty
situations as a base line for monitoring of interventions impacts for specific area.
Therefore, identification of the major factors of poverty could help to distinguish the intervention
directions and inform policy options for tackling poverty by understanding determinants of
poverty from location specific context. In addition it could improve the information and
knowledge gaps that were hampering in identifying and addressing the poor appropriately in the
implementation of development program/project.
The proposed study has tried to address some of the issues related to poverty and determinants of
urban poverty in the study area. In doing so the current study had dealt with identifying and
analyzing major determinant factors of poverty in the town which were crucial for cognitive,
analytical, policy making and evaluation purposes.
1.2. Statement of the Problem
In Ethiopia, poverty is the general feature of the nation causing many sufferings to the largest
proportion of the population. It is a serious agenda for the government, donor agencies, NGOs
and other actors to reduce the level and mitigate the effect and its associated impacts on the
wellbeing of the people. The Ethiopian government has been formulating and implementing
various policy statistics and programs since 1991 that are in one-way or another related with
poverty reduction. Yet most efforts have been biased towards rural areas (Tesfaye, 2006).
Poverty has many faces, such as hunger, lack of shelter, being sick and not being able to see a
doctor, not being able to go to school, not having a job, fear of the future, living one day at a
time. Poverty is losing a child to illness brought about by unclean water. Poverty is
powerlessness, lack of representation and freedom. Poverty has many features; changing from
place to place and across time, and, has been described in many ways. ―Poverty is the inability to

18. 5
retain a minimal standard of living, measured in terms of basic consumption needs or some
income required for satisfying them (World Bank,2006) .
In the towns thousands of people exist in desperate poverty without access to adequate shelter,
clean water, education and health care and basic sanitation. The economic activity and social
infrastructure of the town is low and the living standard of the inhabitant is not in a good
condition. This is due to excessive rural-urban migration, population growth, limited
infrastructure and technical skill. As well, interruption of the electric power, communication
network and water supply. More of dwellers are engaged in occupation which has limited
returns. This include large number of the residents employed in civil service, small scale
industries (wood work and metal work) and in a number of petty business of preparing and
selling the traditional popular drink- tella, arekie and teji.
Debre Birhan town‘s poverty situation is very severe, as it is recognized from several indicators
of poverty like high unemployment level, poor sanitation system, inadequate pure water supply,
inadequate electric power supply, and low wage employment for daily laborers, large percentage
of population with low-income earning, inadequate health facilities, poor infrastructural facilities
(roads, networks and etc), poor housing services, and etc. In sum, the entire above problem
directly or indirectly have implication of urban poverty in the town.
For strategy formulation, it is important to understand who the poor are, where they live, and
what their source of livelihoods is. Poverty is multi-dimensional and extends from low levels of
income and consumption to poor health and lack of education to other ‗non-material‘ dimensions
of wellbeing, including gender disparities, insecurity, powerlessness and social exclusion. A
good understanding of the nature of poverty enables a comprehensive exploration of poverty
determinants. Poverty reduction is the most urgent task facing humanity.
Urban and rural poverty studies were conducted in many places like (Dire Dawa, SNNP, Bahir
Dar, South West shoa, Tigray and other regions). However, study on urban poverty in north shoa
particularly in Debre Birhan town was not conducted before by combining both quantitative and
qualitative methods of poverty assessment. The study of poverty on other regions reinforced the

19. 6
same idea that a broad perspective on the problems of poverty allows us to examine their
multiple factors contributing to urban poverty.
The objectives of most studies in the country are to measure the severity and intensity of poverty
at country level and do not well explain the social well-being dimensions of poverty in the town.
This in turn create difficulty to understand clearly and capture the process of getting poor, the
way out of poverty (the potentials) in addition the effect of individual household persist in
poverty in the town. Therefore conducting a research by combining of quantitative and
qualitative methods helps to understand and identify the determinants of poverty at household
level. It could also assist to have successful poverty reduction intervention by targeting the poor.
This could create opportunity for improving effective and efficient utilization of resources of
program/projects even other than PSNP. Problem and determinants of poverty in different urban
areas and/or cultural settings will differ. And hence there is a need for specific urban poverty
studies. This paper aims to add to the discussion by examining the socio-economic correlates
and determinants of poverty in urban Ethiopia.
Furthermore, there are various studies regarding the determinants, nature and problem related
with poverty around the world. But due to the focus of previous studies on rural poverty, the
determinant of urban poverty is not studied very well and most of them are descriptive.
Therefore, this research is intended to examine the determinants of urban poverty in Debre Bihan
town using both descriptive and econometric model by specifying logit approach rather than
descriptive alone. Often we believe that the determinants of poverty differ from one area to the
other, which would mean that there are differences in ―structure.‖ In this case we could estimate
separate regressions for each area for instance, for each region in a country; which calls for
studying the situation for each town separately rather than making generalizations based on the
studies in few urban centers. Having this in mind, this research is designed to investigate the
determinants of poverty in one of the urban areas of Amhara region, Debre Birhan.

20. 7
1.3. Objective of the study
The main objective of the study is to assess the determinants of urban poverty and poverty
conditions in Debre Birhan town. More specifically the study seeks:
 To identify and analyze major determinant factors which dominantly affect urban poverty in
the study area.
 To assess household poverty status in the study areas,
 Drawing possible conclusions and provide policy implications based on the study
findings.
1.4. Significance of the Study
The study is expected to specifying the poor from the non- poor; and this may help in reducing
the prevalence of poverty with targeted interventions in Debre Birhan town. No similar study has
been conducted in this area before as to the knowledge of the researcher. This research,
therefore, will serve as a springboard for future studies. This study could contribute significantly
to improve the planning and implementation processes of targeting the poor. Thus problems of
targeting poor could be easy by assessing the household poverty status.
Finally the results of the study will provide Information to policy makers, planners,
Administrators, and development institutions, or for any interested stakeholders/actors who in
one way or another are engaged in the development of the city, facilitating future investment
efforts to review their strategies and provide due effort to reduce urban poverty in the town.
1.5. Scope of the study
Urban poverty was the worldwide issue specially, in less developed country particularly in
Ethiopia. There were also different towns in our country, which faced the problem of urban
poverty. But this paper focused on the specified area. The scope of the study was to the level of
micro-economic level of Debre Birhan town with the selected sample household respondents;

21. 8
and it covered five sample kebeles from the total of nine kebele administrations of Debre Birhan
town. The study also covered relevant socio-economic and demographic characteristics of
households. The major assessments of the study focused on identifying the main determinants
that lead urban households to Poverty.
1.6. Limitation of the study
Some sensitive variables such as income and properties (assets) was not to be correctly obtained
and valued since few respondents were not willing to tell their actual income and income status.
The respondents were tend to overestimate their expenditure and under estimate income. The
responses, therefore, was not 100 percent perfect. Urban poverty was a function of multitude
factors. In this study, only some variables, which will assumed to be the main determinant
factors of urban poverty, was included. Due to financial, time and other resource scarcities and
limitations the study did not cover other dimensions of poverty.
1.7. Organization of the Paper
The research paper was organized into five chapters. The first chapter dealt with background,
statement of the problem, objectives of the study, research questions to be addressed, and
significance, Hypothesis, scope and limitations of the study. The second chapter also discussed
on review of conceptual as well as empirical literatures pertinent to objectives of the study.
While, chapter three exclusively dealt with the research methodology pursued, chapter four also
was presented on some general features of the study area, major findings and discussion. Finally,
the conclusion and policy implications were presented in chapter five.
1.8. Hypothesis
This study had two main variables: the dependent (regressed) and independent (explanatory
variables. The regressed variable was urban poverty dummy 1 if the household is poor and 0
otherwise. And the other was the independent/explanatory variables of dummy and continuous
variable which they had a significant role in determining urban poverty in Debre Birhan town. In

22. 9
the course of identifying the main determinants of urban poverty, the main task was to analyze
the negative and positive effects of explanatory variables on the dependent. And the researcher
was stand by hypothesizing as follows
1. Household Head Sex (hhsex): It is widely believed that the gender of the household head
significantly influences household poverty, and more specifically, the households headed by
women are poorer than those headed by men. Thus for this study female headed HHs was
hypothesized to be positively related with the likelihood of being poor.
2. Household Head Age (hhage): This refers to the age of the head of the household measured in
years. It is hypothesized that young head of a household could generate income for the family by
participating in employment generating scheme than the old age. Thus old aged head of family
could possibly be trapped in to food poverty because of not participating in employment
generating scheme. Therefore, age is hypothesized to be negatively related with the likelihood of
being poor.
3. Household head marital status (hhmarstat): if household head is married they have better
opportunities in economy than those divorced and widowed. This variable will negatively affect
the level of poverty status.
4. Household Head Education (hhedu): household with more educated are expected to have an
advantage in searching of information and getting jobs. Therefore education has hypothesized to
negatively affect the poor.
5. Household Head Income (hhpci): This refers to the amount of income earned in Birr from
Different type of activities. In reality a household who earn less income is more likely to be poor.
And hence, this variable will hypothesize to be negatively related with the likelihood of being
poor.
6. Household Head Occupation (hhoc): This refers to the type of occupation that the household
head is engaged in. if the household head is self employee and Government/NGO employee they

23. 10
will earn more and can lead their life to the better than those laborer and pensioner. Therefore
occupation will posetively affect the level of poverty status.
7. Household Family Size (hhfmsz): This is the number of regular family members that live
under the same roof. Large and extended family size may affect the HH income negatively;
which could affect and determines the level of poverty in relative ways because of imbalance
between production and consumption. According to Hilina (2005) households with bigger family
size are more likely to be poor than household with relatively small family size. Therefore,
family size is hypothesized to be positively related to HH poverty status.
8. Household Health (hhhstt): household head with sick household member will in worse than
those with a healthy ones in any dimensions of life. The more frequently the household member
gets sick and it is possible to have higher expense for medication. Therefore, this variable will
posetively relate with household poverty status.
9. Household House Tenure (hhht): It is hypothesized that households without their own house
they may face social and economic problem. Therefore house will negatively affect the level of
poverty status.
10. Household water ownership (hhwo): household who don‟t have private tap water in their
compound was more likely to be poor than who have, and this variable have negatively related
with poverty status.
11. Household source of energy/electricity (hhele): There is a striking difference in the
percentage of the population with access to electricity as a lighting source across the urban
spectrum. Access to electricity is mainly an issue of overall availability. The researcher may
expect that this independant variable may affect negatively the dependant variable.

24. 11
CHAPTER TWO
2. LITERATURE REVIEW
2.1. Theoretical Literature Review
Historically, poverty has been related to income, which remains at the core of the concept today.
However, ―income‖ is itself no less problematic a concept than ―poverty‖; it too has to be
carefully and precisely elaborated. Other resources such as assets, income in kind and subsidies
to public services and employment should be imputed to arrive at a comprehensive but accurate
measure of income. People can be said to be in poverty when they are deprived of income and
other resources needed to obtain the conditions of life—the diets, material goods, amenities,
standards and services—that enable them to play the roles, meet the obligations and participate
in the relationships and customs of their society(UNDP,2006).
2.1.1 The Concept and Definition of Poverty
The word ―poverty‖ and / or ―poor‖ originated from the Latin word pauper meaning poor, which
has its roots in the words pau- and pario that is ―giving birth to nothing‖; referring to
unproductive livestock and farmland (Westover, 2008). Historically, the idea that some people
are trapped in poverty while others have spells in poverty was a central element of most analysis
(Hulme and Mckay, 2005). For example, officials and social commentators in eighteenth century
France distinguished between the paver and the indigent.
The former experienced seasonal poverty when crops failed or demand for casual agricultural
labor was low. The latter were permanently poor because of ill health (physical and mental),
accident, age or alcoholism. The central aim of policy was to support the pauvre ways that would
stop them from becoming indigent (Hulme and Mckay, 2005). In contemporary times this
durational aspect of poverty has been relatively neglected and conceptual development, and more
particularly measurement, has focused on severity/depth and multidimensionality.

25. 12
This has been especially the case in economies where serious work on duration only began to
emerge in the late 1980s (Gaiha 1993). An implicit assumption of much research was that the
persistence of poverty at the individual and household level was highly correlated with the
severity of poverty. During the early 1990s such work began to proliferate based on available
panel data sets, and in 2000 the first collection of papers on this topic was published (Baulch and
Hoddinnott 2000).
This has been especially the case in economies where serious work on duration only began to
emerge in the late 1980s (Gaiha 1993). An implicit assumption of much research was that the
persistence of poverty at the individual and household level was highly correlated with the
severity of poverty. During the early 1990s such work began to proliferate based on available
panel data sets, and in 2000 the first collection of papers on this topic was published (Baulch and
Hoddinnott 2000).
Poverty is hunger. Poverty is lack of shelter. Poverty is being sick and not being able to see a
doctor (World Bank, 2005). Poverty is losing a child to illness brought about by unclean water.
Poverty is powerlessness, lack of representation and freedom (World Bank, 2005). According to
the Ghana Poverty Reduction Strategy (GPRS) (2004), poverty is now recognized as multi-
dimensional with complex interactive and causal relationship between the dimensions.
According to SIDA (2005), the poor often lack access to finance and income-earning
opportunities.
The subject of poverty has been a major issue on both national and international scale
discussions, predominantly among the developing countries (Balogun, 1999). According to
Balogun (1999), poverty could be described as a condition where a society barely survives on a
level of subsistence, coupled with limited access to the necessities of physiological factors such
as clothing, food, and appropriate accommodation, in view of maintaining a basic standard of
living. In the view of the World Bank and The World Development Report (WDR), observations
made suggest that conditions could be expressed as poor if people live on a per capita income
lower than US $370 at any given time (WDR, 1999) or as being extremely poor by living on less
than US$ 1 per day, and moderately poor by living on less than US$2 daily (World Bank, 2007).

26. 13
The statement also projects that ―1.1 billion people in 2001 had expenditures below US$1 a day
and 2.7 billion lived on less than US$2 a day‖. Poverty as a condition is not only confined to
developing nations, but it‘s also a universal phenomenon that could be observed in a set of social
problems including homelessness and the persistence of "ghetto" housing clusters(World
Bank,2007).
In attempting to summarize the definition of poverty, Englama and Bamidele (1997) asserted that
poverty in both relative and absolute terms refers to a circumstance where a person is not able to
fend or provide sufficiently for his or her necessities or fundamental human requirements such as
clothing and decent accommodation, food, the fulfillment of social and economic
responsibilities, non-access to productive employment, lack of skills, resources and confidence;
and has restricted admission to economic and social infrastructure. These include access to
health, education, potable water, sanitation, and roads. These preclude the person from
advancing in welfare which is limited by the scarce availability of economic and social
infrastructure. They concluded by terming this situation as being subject to a ―lack of
capabilities‖ (Englama and Bamidele, 1997).
Poverty is the scarcity or the lack of a certain (variant) amount of material possessions or money.
Poverty is a multifaceted concept, which may include social, economic, and political
elements. Absolute poverty, extreme poverty, or destitution refers to the complete lack of the
means necessary to meet basic personal needs such as food, clothing and shelter.
The threshold at which absolute poverty is defined is considered to be about the same,
independent of the person's permanent location or era. On the other hand, relative poverty occurs
when a person who lives in a given country does not enjoy a certain minimum level of "living
standards" as compared to the rest of the population of that country. Therefore, the threshold at
which relative poverty is defined varies from country to another, or from one society to another.
Providing basic needs can be restricted by constraints on government's ability to deliver services,
such as corruption, tax avoidance, debt and loan conditionality‘s and by the brain drain of health

27. 14
care and educational professionals. Strategies of increasing income to make basic needs more
affordable typically include welfare, economic freedoms and providing financial services.
2.1.2 Context of Urban Poverty
There are a number of common misconceptions about urban poverty. According to Deniz
Baharoglu and Christine Kessides, It is important to correct these misconceptions, and they
establish some basic premises about urban poverty as follows:
Urban poverty is not necessarily an indication of economic failure. Urban poverty can to some
extent reflect active rural–urban migration. This is because cities offer better opportunities for
individuals to improve their welfare. Indeed, cities have historically served poor people as
platforms for upward mobility.
Internal migration is not a major variable explaining urban poverty. Controlling migration is not
a valid policy response to urban poverty. Studies of internal migration in many countries reveal
that migrants are not necessarily among the poorest members of their original or receiving
communities. There are no simple relationships between migration and poverty. Policies that aim
to restrict internal migration hurt the poor and the overall labor market and are usually
ineffective (de Haan 1999 and 2000).
Urban conditions cannot be generalized across types of urban areas. Cities of different sizes tend
to have different problems. This suggests that those public policies that in the past seemed to
favor certain cities are not counterbalancing the pressures of population growth on service
capacities; nor are they addressing the failures of urban governance.
The concept of ―city‖ is heterogeneous. Average welfare indicators presenting overall urban
conditions cannot give a correct picture of poverty within a city. In cities, the poor and rich—
with their different levels of assets—live together, and there are significant intra-urban
differentials in social, environmental, and health conditions. Manifestations of poverty in urban
areas can be strongly site-specific. It is important to know the social and physical conditions of

28. 15
different groups and neighborhoods within the city, the forms of deprivations that they suffer,
and their numbers and characteristics.
The urban poor are a diverse group. The urban poor comprise different groups with diverse needs
and levels and types of vulnerability. These differences may be traced to factors such as gender,
physical or mental disability, ethnic or racial background, and household structure; they also
relate to the nature of the poverty itself (for example, long-term or temporary).
2.1.3 Determinants of poverty
Table 1: Main determinants of poverty
Main determinants of poverty
Regional characteristics Isolation/remoteness, including less infrastructure and poorer
access to markets and services. Resource base, including land
availability and quality. Weather (e.g. are typhoons or droughts
common) and environmental conditions (e.g. frequency of
earthquakes)
Regional governance and management, Inequality
Community
characteristics
Infrastructure (e.g. is there piped water, access to a tarred road)
Land distribution. Access to public goods and services (e.g.
proximity of schools, clinics) Social structure and social capital
Household characteristics Size of household ,Dependency ratio (i.e. unemployed old and
young relative to working age adults) Gender of head; or of
household adults on average
Assets (typically including land, tools and other means of
production, housing, jewelry)
Employment and income structure (i.e. proportion of adults

29. 16
employed; Type of – wage labor or self employmnt; remittance
inflows)
Health and education of household members on average
Individual characteristics
Age
Education , Employment status, Health status & Ethnicity
Source: Poverty Manual, All, JH Revision of August 8, 2005
2.1.4 Causes of Poverty
Poverty can only be fought in the presence of strong institutions, and equitable distribution of
resources. This requires a non-corrupt government. However, in Africa, programmes designed to
fight poverty are not fully implemented because the funds end up in the hands of corrupt
individuals, who pocket the majority. Again due to poor governance, those in authority have
failed to apprehend the corrupt. This creates an imbalance in society and leads to more poverty
because you end up with a few influential and powerful individuals oppressing the poor (who are
the majority). Another leading cause of poverty in Africa is the prevalence of diseases (such as
malaria, HIV/AIDS, TB, EBOLA etc) due partly to inadequate and poor health facilities. When a
household is affected by any of the diseases, the little resources are spent on treating the sick. In
a worst case scenario where the bread winner dies, those who are left behind have no resources
to support themselves, thus leading to poor lifestyles. The loans given out by the World Bank
and IMF have also contributed to the poverty in Africa. Such loans come with strict
conditionality, which usually require governments to adjust some of their economic decisions.
For instance, the requirement to reduce total government spending in African countries has
affected major social sectors such as education, health and infrastructure, which are drivers of
economic development (Alex Addae-Korankye, 2014).

30. 17
2.1.5 The difference between absolute and relative poverty
Absolute poverty is measured relative to a fixed standard of living; that is, an income threshold
that is constant across time. Absolute poverty measures are often used to compare poverty
between countries and then they are not just held constant over time, but also across countries.
The International Poverty Line is the best known poverty line for measuring absolute poverty
globally. Some countries also use absolute poverty measures on a national level. These measures
are anchored so that comparisons relative to a minimum consumption or income level over time
are possible (Max Roser and Esteban Ortiz-Ospina, 2013).
Relative Poverty, on the other hand, is measured relative to living standards in a particular
society, and varies both across time and between societies. The idea behind measuring poverty in
relative terms is that the degree of deprivation depends on the relevant reference group; hence,
people are typically considered poor by this standard if they have less income and opportunities
than other individuals living in the same society.
In most cases, relative poverty is measured with respect to a poverty line that is defined relative
to the median income in the corresponding country. This poverty line defines people as poor if
their income is below a certain fraction of the income of the person in the middle of the income
distribution. Because of this, relative poverty can be considered a metric of inequality-it
measures the distance between those in the middle and those at the bottom of the income
distribution.
Relative poverty can be measured using the poverty headcount ratio and the poverty gap index.
Indeed, these indicators are common in Europe. However, it is important to bear in mind that
these are not comparable to the estimates published by the World Bank—the nature of the
International Poverty Line is different (Max Roser and Esteban Ortiz-Ospina, 2013).

31. 18
2.2 Empirical Literature Review
2.2.1 Poverty in Ethiopia
Ethiopia is one of the World‘s poorest countries. Out of a population of around 80 million (2008)
people, 35 million people are living in abject poverty. In one of the world‘s poorest countries,
where about 44 percent of the population lives under the poverty line, more than 12 million
people are chronically or at least periodically food insecure. With 80% of Ethiopians dependent
on agriculture as their main livelihood, severe arid conditions due to persistent lack of rainfall
coupled with civil disputes have worsened Ethiopian poverty (World Bank, 2017).
The literature dealing with poverty in Ethiopia is limited, reflecting the lack of an appropriate
and reliable household survey data that would allow the comparison of welfare across time.
Since the early 1990‘s, however, periodic household surveys have been conducted that have
facilitated the analysis of both urban and rural poverty. One of the earliest attempts to examine
urban poverty in Ethiopia was by Mekonnen (1996) using the 1994 Ethiopian Urban Household
Survey (EUHS). The survey provided, among other things, information on the demographic and
consumption behavior of 1,500 households randomly selected from seven urban centers of the
country.
The analysis in Mekonnen (1996) was limited to food poverty in recognition of the fact that
satisfaction of basic food requirements remains a major problem for poor households in Ethiopia.
Food poverty line estimates were obtained in accordance with the food energy intake method,
whereby total expenditure on food is regressed on calorific consumption (Greer and Thorbecke
1986). The findings confirm the hypothesis that there is abject poverty in urban Ethiopia, with 39
percent of the urban population living below the food poverty line. The analysis indicates that the
highest incidence of poverty was recorded for the city of Awassa, followed by Addis Ababa,
Dessie, Mekelle, Jimma, Bahir Dar, and Dire Dawa.
Mekonnen (1999) analysed the determinants and dynamics of urban poverty using the 1994,
1995 and 1997 rounds of the EUHS. The measure of welfare used in the study was consumption

32. 19
per adult equivalent while the estimate of the poverty line was obtained following the cost of
basic needs approach. Thus, a consumption basket that would meet a minimum energy
requirement of 2200kcal of energy per adult per day was constructed and its cost calculated at
region specific prices to obtain the food poverty line. The food poverty line was then scaled up to
obtain the total poverty line. This was done by dividing the food poverty line by the average food
budget share of households in the neighbourhood of the food poverty line.
The analysis in Mekonnen (1999) indicated an increase in poverty between 1994 and 1995 and
then a decrease from 1995 to 1997. Tadesse concluded that price stabilization policies were
important in abating poverty as the observed fluctuations in the standard of living were mainly
triggered by movements in prices, especially that of grains. Such a policy prescription should be
treated cautiously as attempts to stabilize prices may create market distortions. Tadesse (1999)
also advocated human capital development and family planning programs as instruments to
fightagainst poverty.
2.2.2 Identifying Poverty Lines
Given an appropriate measure of welfare, the identification of the poor necessitates that a
poverty line be determined below which individuals or households are considered poor. There
are a number of ways that such a poverty line may be identified. The most common approach is
to estimate the cost of a consumption bundle for which basic consumption needs will be met.
This is known as the cost of basic needs approach. It proceeds by first estimating the food
expenditure necessary to attain some recommended food energy intake. This expenditure level
can be considered as the food poverty line. Next, an allowance is made for non-food goods to
arrive at the total poverty line (Ravallion 1994; Lipton and Ravallion 1995).
Deriving the poverty line using the cost of basic needs approach, however, presents some
difficulties. For example, setting the food energy requirement may be problematic as there are
significant variations among people in physical features and work habits. This renders the task of
setting a minimum energy requirement, even for a specific group in a specific region, daunting.
Even after a minimum requirement is set, there still remains the problem of choosing a food

33. 20
bundle that meets it (Sen 1999). A bundle that meets the requirement at minimum cost (given
prevailing prices) could be chosen, but such a bundle is of little relevance if it is not in tune with
the eating habit of the poor. According to Sen (1999), the actual incomes at which specified
nutritional requirements are met will depend greatly on the consumption habits of thepeople in
question‘. The second difficulty associated with deriving the basic needs poverty line isin
making an allowance for non-food goods. This stems from the fact that there is nothing that
can serve the same role as food energy requirements in anchoring the non-food component of the
poverty line (Ravallion 1994).
In practice, two methods have been commonly used to derive the poverty line; the ‗food energy
intake‘ and ‗food share‘ methods (Ravallion 1994). Both approaches are based on the assumption
that there is a minimum energy requirement for a typical person to keep up normal activities,
such as the 2,200 Kcal per day threshold stipulated by the World Health Organization (1985).
Thus, the ‗food energy intake‘ method attempts to identify the total consumption expenditure at
which a person is expected to attain the minimum food energy requirement. This is accomplished
by regressing calorie intake on consumption expenditure or income. The poverty line, then,
becomes that level of total expenditure at which the minimum energy requirement is met (Greer
and Thorbecke 1986; Ravallion 1994). The advantage of this method is that it automatically
includes an allowance for non-food goods, circumventing one of the difficulties mentioned
above. However, it may lead to an ‗inconsistent poverty comparison across sub-groups or over
time since people with the same command over basic consumption needs will not in general be
treated the same way‘ (Lipton and Ravallion 1995).
In the ‗food share‘ method, the cost of the food bundle that meets the minimum energy
requirement is estimated for each population sub-group. These food poverty lines are then
divided by the share of food in total expenditure of the poorest households, such as the poorest
decile, in each sub-group to obtain the total poverty line. This method may also lead to
inconsistencies in poverty comparison since the share of food in total expenditure does not
remain constant across sub-groups (Ravallion 1994).

34. 21
2.2.3 Main indicators used to measure poverty
Identifying the poverty line facilitates identification of the poor. The next important issue is the
choice of an appropriate poverty measure to aggregate the information on individual‘s welfare.
The earliest and perhaps most famous measures of poverty are the head count ratio and the
poverty gap measure.
The “poverty headcount ratio” p0
The most straightforward way to measure poverty is to set a poverty line and count the number
of people living with incomes or consumption levels below that poverty line and divide the
number of poor people by the entire population. This is the poverty headcount ratio (Max Roser
and Esteban Ortiz-Ospina, 2013).Measuring poverty through the headcount ratio provides
information that is straightforward to interpret; it tells us the share of the population living with
consumption (or incomes) below the poverty line are. But measuring poverty through headcount
ratios fails to capture the intensity of poverty – individuals with consumption levels marginally
below the poverty line are counted as being poor just as individuals with consumption levels
much further below the poverty line.
The head count index measures the proportion of the population falling below the poverty line.
This ratio, according to Kimalu et al., (2002), however, has some shortcomings. First, it does not
show how far below the poverty line the poor are; that is, it ignores the inequality among the
poor. Second, it forces the overall poverty index to remain constant even when the welfare of the
poor has improved or worsened. Third, with this index, an income transfer from an extremely
poor person to a person just below the poverty line (enabling them to cross the line) would show
a reduction in poverty despite the decline in the income of the extremely poor. The poverty gap
index is an alternative way of measuring poverty that considers the intensity of deprivation.

35. 22
The 'poverty gap index' P1
The most common way to measure the intensity of poverty is to calculate the amount of money
required by a poor person to just reach the poverty line. In other words, the most common
approach is to calculate the income or consumption shortfall from the poverty line. To produce
aggregate statistics, the sum of all such shortfalls across the entire population in a country
(counting the non-poor as having zero shortfalls) is often expressed in per capita terms. This is
the mean shortfall from the poverty line. The 'poverty gap index' takes the mean shortfall from
the poverty line, and divides it by the value of the poverty line. It tells us the fractions of the
poverty line that people are missing, on average, in order to escape poverty. The poverty gap
index is often used in policy discussions because it has an intuitive unit (per cent mean shortfall)
that allows for meaningful comparisons regarding the relative intensity of poverty (Max Roser
and Esteban Ortiz-Ospina, 2013).
The poverty gap index (P1) measures the extent to which individuals fall below the poverty line
(the poverty gaps) as a proportion of the poverty line. The sum of these poverty gaps gives the
minimum cost of eliminating poverty, if transfers were perfectly targeted. This measure does not
reflect changes in inequality among the poor. This measure determines the depth of poverty but
ignores its severity (M.K. Jayamohan & Amenu Temesgen Kitesa, 2014).
P1 is an index that measures the extent to which the incomes of the poor lie below the poverty
line. It measures the intensity of poverty by averaging the distance between the expenditure of
the poor persons and the poverty line. According to Kimalu et al., (2002), since the index
measures the shortfall of the average income of the poor relative to the poverty line, it can be
used to estimate the resources that would bring the expenditure of every poor person up to the
poverty line thereby eliminating absolute poverty.
The „Poverty severity index‟ p2
P2 is an index that shows the severity of poverty by squaring the gap between the expenditure of
the poor individual and the poverty line. Because the index gives more weight to the poverty of

36. 23
the poorest, it measures the degree of inequality among the poor implying that transferring
income to the poorest from the better-off poor should lower the poverty index (MEDaC, 1999b).
It increases more than proportionately with the poverty gap. The larger the poverty severity
index as measured by P_=2, the greater the poverty gap, which, indicates that poverty is severest
among the very poor (Kimalu et al., 2002).

37. 24
CHAPTER THREE
3. RESEARCH METHODOLOGY
This chapter included the descriptions of the study area, research methodology-data sampling
Procedure, method of data analysis, and definitions of variables.
3.1. Description of the Study Area
Debre Birhan town is the oldest town founded by Emperor Zera Yaqob around 1456 A.D.
According to the chronicler of Emperor Zara Yaqob, Debre Birhan was founded by the Emperor
Zara Yaqob as a capital for his empire in 1456 in connection with the appearance of Orthodox
Church which was ordered and established by the Emperor in response to a miraculous light that
was seen in the sky. During this time its name was Debre Eba, which was changed in to the
present name (that is Debre Birhan) during his reign in association with that light descended on it
(near the present Debre Birhan Sellassie Church). Historical development of Debre Birhan was
up and down in the past 5 or 6 centuries from its establishment. The town is located at 130 km
towards the North East of Addis Ababa city and it is the capital city of Ethiopia North Showa
Zone of Amhara Region. Astronomically, the town is positioned at 9°41' North latitude and
39°40' East longitude and characterized by cool temperate climate.
The town is bounded by weredas of North Shewa Zone of ANRS which is an indication of good
potential. Currently, it is classified with 9 kebeles under municipal status and wereda level and
serves as a center for North Shewa Zone and Basonna Wereda too. The total area of Debre
Birhan under the municipal (wereda level) jurisdiction (including the surrounding rural areas) is
estimated to be about 18,000 hectares while the existing built-up area under urban occupation is
some about 2200 hectares that, in general, implies the available excessive expansion areas within
its jurisdiction. The shape of the town, as identified in the existing study, is somewhat linear
(elongated) following inlets and/or outlets of major roads (Da-Ya, 2014).

38. 25
With an average elevation of 2750 meter above sea level (m.a.s.l), Debre Birhan is classified
under Dega agro-climatic zone. Debre Berhan city is one of the coolest cities which are found at
sub tropical zone of Ethiopia. With an average maximum temperature of 20.1o
c and average
minimum temperature of 6.5o
c, the town has got mean annual temperature of 13.3o
c (2008 to
2013 G.C). This, though may be cold for some times (October, November and December), is
favorable for human settlement and to undertake any developmental activities. Debre Birhan,
with mean annual rainfall of 965.25mm (2008-2013 G.C), has moderate annual rainfall. The
dominant prevailing wind directions of Debre Birhan are Southeasterly and Easterly winds (that
blow from southeast to southwest and from east to west). (EMA, 2014 as cited in Da-Ya, 2014).
The population size in the town, according to the 2007 National Census, was about 65,231 of
which 31,668 (48.5%) were males and 33,563 (51.5%) females. According to the 2015
Population Projection values at zonal and wereda levels, Debre Birhan town administration,
2015-16 by Central Statistical Authority (CSA) makes the town with the population of 92,889
and that contain 6,032 elders that lives in different economic back ground. Regarding the
religious composition of Debre Birhan town residents, the majority (94.12%) of the inhabitants
were Ethiopian Orthodox Christian, while the rest (3.32%) of the populations was Muslim and
2.15% were Protestants (CSA, 2007).
According to CSA (2013), the latest estimation of Debre Birhan‘s population reached that of
83,479. Between 1984 and 1994 population size of the town grew at the average rate of 4.12%
per annum, and from 1994 to 2007 its rate declined to 4.01% per annum. According to CSA
(2014), the population size in the town was estimated about 84,944 of which 41,248(48.6%)
were males and 43,696(51.4%) females. From the total population 72532(85.4%) lives in urban
around the center where as 12,412(14.6%) were urban periphery. The town is classified in 9
kebeles under municipal status of which 5 sub kebeles were urban periphery.
In any case, all these entailed an alarmingly population growth and increase and if such
increasing of population size continuous without any doubt within a certain years. The reason
behind such increment might be its high natural growth and in migration to the town since the

39. 26
area is becoming economically active and very much convenient for investment as compared to
the area around it. Thus, such issue require due attention.
Figure1: map of the study area
3.2. Data types and sources
This research made use of both primary and secondary data. Primary data were collected from
randomly selected households of 4 kebeles. As part of the primary data collection effort, the
sample based household level data collection work was undertaken using pre-prepared structured
questionnaire. Primary data were collected from the sampled respondents on different issues
such as sex, age, marital status, health status, and education levels etc. Moreover, at sample
household level, information collection was included average monthly household income and
expenditure, family size, housing condition, fuel types and sources used for cooking, and all
other variables hypothesized to determine urban poverty in the study area.

40. 27
Secondary data was collected from various sources such as reports, research center, CSA,
woreda Administrative office, internet and other published and unpublished materials, which was
found to be relevant to the study. That was, relevant literature (including previous studies), was
reviewed consisting the issues under consideration. The questions from the structured
questionnaires were posed to the sample heads of households to collect appropriate data. Hence,
the collected data was analyze and interpreted using appropriate statistical methodologies and
presentation techniques.
3.3. Sample Size determination and Sampling Technique
The study was employed cross-sectional survey to asses determinant of urban poverty in Debre
birhan, taking into account that there are tradeoffs between cost and accuracy in every research,
the total number of samples was determined by applying a simple formula (Yamane, 1967).
Multi-stage sampling procedure was used to select the total number of samples. The first stage
involved stratification of the town consisting of nine kebeles in to two spatial distributions (urban
dwellers and urban periphery dwellers) for representativeness of the sample households.
According to Debre Birhan city service (2016), kebele 02, 03, 04, and 05 were categorized under
urban dwellers which live in center and the rest kebele 01, 06, 07, 08, and 09 were half and
above of them were urban and others were categorized in urban periphery dwellers far away the
center. Based on this division, four kebeles has been taken for collecting data. The sampled
kebeles were 01, 02, 03, and 09. They are selected randomly to have two kebele from each
division. The sample size in each kebele will be taken proportional to total population in each
kebele. Once again a random sampling technique was applied to select the representative sample
respondents (households).

41. 28
Table 2: Total sampled household‘s proportion to the total population size from sampled kebeles
No Sampled
Kebeles
Total
population(N)
Sampled
households(n)
Percentage
share
1 01 4,220 20 9.85%
2 02 15,038 70 34.48%
3 03 11,236 53 26.11%
4 09 12,553 60 29.56%
total 43,047(100%) 203(100%) 100%
Source: own computation
Whereas n = Household sample size
N = Total household population size =43,047
e = Degree of precision = 7%
= with the given level of confidence 93%
n = 203
3.4. Data collection methods and procedures
The researcher was taken 4 sample kebeles from a total of nine urban kebeles of debre birhan
town. The sample was determined using the minimum sample size formula. That was, a total of
given sample household respondents were selected and asked for collecting the necessary
information in the study. This study also was used a cross-sectional survey to assess the
determinants of urban poverty in Debre Birhan town. In addition, a random sampling technique
was employed to conduct for the study.

42. 29
3.5. Method of data analysis
3.5.1 Descriptive analysis
To accurately portray; the situation of demographic and socioeconomic variables of the
households descriptive analysis was made. The analysis was used to assess the overall livelihood
of the population in the town. The specific method of data analysis involved including tabulation
and cross tabulation, frequency, percentages. To support the analysis, the researcher used
different tables, graphs, and figures.
3.5.2 Model Estimation
To measure poverty and identify the poor from the non poor, we utilized empirical models. In
order to attain the objectives, the study was made use of cross-sectional household survey data
was collected from social societies of Debre Birhan town from the selected sample households.
The data collected was also analyzed and discussed applying poverty index, descriptive statistics
and Binary logit regression model analyses.
3.5.2.1 Model Specification
Logit model estimation approach was used to look at the effects of household characteristics on
the risk of being poor or not poor. The dependent variable in this logit model assumed a value of
either one or zero depending on whether a household is poor or not. Binary logit model was used
to analyze the relationship of household‘s poverty status and its determinants. Thus, a household
is deemed living in poverty (Y = 1) if it‘s total consumption per adult equivalent per year is less
than the poverty line or non-poor (Y = 0) if its consumption short fall is greater than or equal to
zero. The dependent variable is defined as the binary outcome of an unobserved underlying
latent variable. Logit model is appropriate when we assume the random components of response
variables follow binomial distribution and when most variables have categorical responses. The
dependent variable in the model is dichotomous. The logit model expresses the dependant
variable as a function of a set of explanatory variables k in the following form:

43. 30
Y 1 X12 X3 X3 k X k (1)
iX1 run from 1X1 to  k X k
Where *
Yi = latent variable that indexes the measure of poverty;
*Yi =1 if Yi < 0, 0 if yi > 0; n is the number of explanatory variables;
is the intercept; is the coefficient vector of all explanatory variables;
 is the disturbance term; and Xi is explanatory variables.
Aggregating the value yields
In practice Y is unobserved, and is symmetrically distributed with zero mean and has
cumulative distribution function (CDF) defined as F (). What we observe is a dummy variable
y, a realization of a binomial process defined by
y=1 if y>0 and 0 otherwise
Explanatory variables includes: Age, household head Sex, household head marital status,
household family size, education, household health status, household water Ownership,
household house tenure, household per capita income, household head Occupation, and
electricity.

44. 31
3.5.2.2 Description of variables
Table 3: Description of explanatory variables in the logistic model
Variables Code Type Description
Dependent variable
Poverty status Povstat Dummy 1= if household is poor
0= otherwise
Explanatory variable
Household head age Hhage Continuous Age of the household in number.
Household head sex Hhsex Dummy 1= if the household head is female ,
0= otherwise
Household family size Hhfmsz Continuous Number of person in the household
Household head marital
status
hhmarstat Dummy 1= if the household head isn‘t
currently with spouse, 0= otherwise
Household head
education
Hhedu Continuous Education level of the head in years
of schooling
Household head
occupation
Hhoc Continuous Types of sectors(activities)
household engaged in.
Household water Hhwo Dummy 1= if the household hadn‘t private
own piped water, 0= otherwise
Household house tenure Hhht Dummy 1= rented 0= otherwise
Household health status Hhhstat Dummy 1= household with frequent patient
member, 0= otherwise
Household per capita
income
Hhpci Continuous Amounts of income earned in birr
Household electricity Hhele Dummy 1= if the household hadn‘t his own
metro electric, 0= otherwise

45. 32
CHAPTER FOUR
4 DATA ANALYSIS, PRESENTATION AND DISCUSSION OF
THE RESULTS
4.1 Identifying the Poor
The food energy intake (FEI) approach is used in the identification of the poor from the non-
poor. This is done based on a predetermined value expressed in terms of calorie intake
equivalents. In the identification of the poor from the non-poor the research used the food energy
intake approaches (FEI) and is preferred to the CBN based on the following premises (EHNRI,
2000). First, during the survey period (May, 2018) the prices of all commodities in the country
and the study area as well have increased drastically. This is not consistent with the prices of the
previous years and hence could not clearly show the reality in the consumption expenditure
behavior of the residents. If only the surveyed time prices of all the commodities and other
materials were taken and the poor were to be analyzed based on this, surely, the figure would be
inflated and the result might be farfetched from the prevailing reality.
Second, a large number of residents, particularly, those who reside in the peripheries of the town
have their own lands (who are urban farmers) do not buy cereals and have little expenditure for
cereals for they consume from what they grow. This could mask the result if the study used the
CBN method which values all costs in monetary terms.
Third, the FEI is preferred to the CBN for the latter needs enumeration and quantification of
basics and non-basics of different items in monetary terms. The problem arises particularly in
estimating the costs of non-basics. No doubt, Debre Berhan is not an exception to this pitfall. It is
not, however, to mean that all residents in the study area were not able to quantify their assets or
commodities in monetary terms nor does they are always smart enough in telling commodities
such as cereals in Kilograms.

46. 33
Economists and development practitioners agree on the perplexities of getting error free method
of setting poverty lines. For instance, the minimum calorie intake requirements for households
(specifically for individuals) in a specified period, though popular, are still flawed with debates.
This is because households are composed of family members with different age and sex leading
to differences in needs, consumption habits, and preferences. It is also true that the same level of
income cannot serve equally the needs of households that are different in composition
(Mohammed, 2017).
To minimize such problems scholars including development specialists have been busy probing
for a number of alternatives among which the adult equivalent scale, which establishes on
equivalence in the consumption of an adult, a child, and extra, is found to be the popular one.
This requires estimation of household consumption expenditure in monetary value. I argue;
however, that in Debre Berhan where households consume both marketable and non-marketable
goods, it is difficult to use equivalent scales generated from preferences revealed only from
marketable goods. Therefore, instead of estimating the costs of consumption expenditure, the
study used the quantities of bundles of items households consumed. To identify the poor
households in Debre Berhan the following six steps are used.
Step one: This step is left for enumeration of food items consumed in the study area. The lists of
food items included in the analysis are: Teff, Wheat, Maize, Barely, beans, peas, Guaya, Lentil,
vegetable (Cabbage, Carrot), Dry Pepper, Edible Oil, Cow Milk, Onion, Butter (Cow and
Vegetable), Meat, and Sugar.
Step Two: Each bundle of food item is weighted with the appropriate unit of measure (in
kilograms or litters). To get the total amount of food bundle a household consumed in a month
each of the weighted bundles of food items are summed up. Teff +Wheat+ Maize+ Barely+
Potato+ Onion+ Beans+ Peas+ Guaya+Vegetable (Cabbage, Carrot) + Dry Pepper+ Edible Oil+
Milk+ Butter (Cow and Vegetable) + Meat+ Sugar. Mathematically it can be represented as,
K1+K2+...+Kn (up to the last food item) where K refers to the value in kilogram or Litter of each
food basket.

47. 34
Step Three: The aggregate value of baskets of food items consumed by a household in a month is
divided to the corresponding sample size of the household to get the amount of kilograms each
adult individual gets in a month.
=
Where Xi is total baskets of different food items in kilograms or litters a household consumed in
a month and Y is the family size of the surveyed household.
Note: To be simple or easy understanding of finding amounts of calorie intake per day at
household level is just finding at individual level at first and deciding the individual is a
representative of household level
Step Four: The amount of Kilograms each household consumes in a month is again divided for
30 days to get the amount of kilograms each adult individual consumed in a day. This is
equivalent to L/30.
Step Five: The amount of kilograms an individual consumed in a day is again converted into
calorie intake and is calibrated to the predetermined 2200 calorie per day per adult equivalent.
The conversion factor for the mentioned food items is indicated in the table below. The quantity
of the bundle of food is determined in such a way that the bundle supplies the predetermined
level of minimum caloric requirement. It is at least 2,200 KCa intakes per day that will leave an
individual not to be poor (MoFED, 2012).

48. 35
Table 4: Calorie Contents of Different Food Items
Consumption per
100grams
Energy in calorie Consumption per
100grams
Energy in calorie
Teff 3 Lentil 325
Wheat 340 Vegetable(cabbage,carrot) 75
Maize 344 Dry pepper 73
Barely 370 Edible oil 900
Potato 75 Cow milk 79
Onion 38 Butter 700
Beans,pea
s
310 Meat 626
Sugar 375
Source: Food Consumption ECSA and Ethiopian Health and Nutrition Research Institute
Note: For foodstuffs of more than one item the average values are taken
Step Six: This is the last step the research used to get the number of poor and non-poor
households in the study area. If X is total calorie intakes of a household in a day and Y is the
family size of the surveyed household in the town, then calibrating the poverty line using the FEI
international agreed figure -2200 calorie per day for an adult person as recommended by
nutritionists, yields: -
= 106 Households (above poverty line
= 97 Households (below poverty line
In the research there exist three indices of poverty as follows:-
1. Head count ratio: p0 = q/N
Where, q is number of person which is under the poverty line.

49. 36
p0 = 97/203 = 0.48
2. Poverty gap: P1= /ZN)
1
= 16.85/203 = 0.083
It is normalized poverty gap which is total poverty gap dived by product of poverty line
and population.
3. Severity gap: P2 = /ZN)
2
, which is called foster greer thorbeck
P2 = 5.28/203 = 0.026
As shown the indices result above, 48% of the sample households are unable to fulfill the
minimum amount calorie intake 2,200 per adult and they live under absolute poverty. Besides, a
poverty gap (a = 1) of 8.3% implies the amount of income transfer needed to close up the
average gap or distance separating the poor from the poverty line. Finally, the FGT severity
index (a = 2) in the food energy intake reveals a 2.6 % fall below the threshold line.
4.2 Descriptive Analysis, Demographic Characteristics of the sampled
Households
Results of the study show that out of the 203 sampled households, 59% are male headed and
41% are female headed. The distribution of the households by marital status shows that 68% of
them were with his/her spouse, the rest 32% were single, and widowed or divorced. The ethnic
composition of the sample households includes 84% amhara, 10% oromo and 6% belongs to
other ethnic group. On the other hand, distribution of religion sample households shows that88%
were Orthodox, 8% Muslim, 3% Christian Protestant and 1% were belongs to other religion
groups.
As mentioned in depth in the literature review, household socio-economic characteristics are
amongst the major determinants of urban poverty. In light of this, household type, sex, age,
marital status, family size , education level, occupation, per capital income, health status,
housing tenure water source and energy power are described below.

50. 37
4.2.1 Sex and poverty
Due to socio-economic factors poor women headed households are greater than men headed
Households. Several studies, such as determinants of poverty in Gondar city studied by
Getachew (2009), and determinants of poverty in Mexico studied by Shewaye (2002) reason out
many factors for the case. Female headed households have less opportunity in monetary income
generation than households headed by men.
Quite several studies have discussed the phenomena of feminizing poverty which assumed that
the prevalence of poverty is higher to female-headed households than male-headed ones.
Different scholars support this assumption by providing various justifications. This could be due
to the presence of discrimination against women in the labor market, or it might be because
women tend to have lower education than men do and therefore they are paid less salaries. Or
else, they are in general deprived the opportunities of exercising when compared to men in many
respects.
In this study, from the total of 203 sample respondents, 41% are female household heads, and
59% are male household heads. Of the total female headed households, 62.6% of them are found
to be poor; and 37.4% are non poor. Of the total male headed households, only 37.5% of them
are poor, and the rest (62.5%) are non poor. The study results obtained about sex of household
heads and poverty status tally with or support the theories of Wratten (1995), Shewaye (2002)
and Mekonnen (2002) that, gender based differentials in vulnerability to illness and violence as
well as women‟s subjected to discrimination in labor markets, in getting credit services, property
ownership, etc. compared to men. Because of these, female-headed households are the most
affected and vulnerable groups to growing urban poverty. This implies that poverty more affects
female household headed ones than their male counter parts.
A crude observation of the figure makes one aware that the number of male headed households
who live below and above the poverty line is much higher in absolute terms than those of
female-headed ones. This is not; however, a strong justification to say they face the hard core
poverty or are leading decent life for the number of male –headed households covered in the

51. 38
survey are much higher than those of females by exactly 1.44 times. A better comparison would
be to see the ratio of poverty sharing between the two sexes. In the above poverty like category
there are 2.41 male-headed households for each female-headed ones. On the other hand in the
below poverty line group there is one female-headed household in every 0.86 male-headed ones.
This shows that the gap between female and male-headed households is only a matter of 0.58,
which means that the incidence of poverty is relatively comparable in the below poverty line
category if not identical.
Nevertheless, the gap between male and female-headed households in the above poverty line is
relatively significant in that most of the male-headed households have escaped from the status of
being in the below poverty line while the females are experiencing more poverty. This result is
inconformity with most literatures, which assume that the probability of falling into poverty is
more as females head a household. The probability that a household will be poor when headed
by females is significant at 95 confidences interval. The study found out that being in a
household of female-headed one is more vulnerable to the prevalence of poverty in Debre Birhan
than those of male headed ones.
4.2.2 Age and Poverty
Some scholars argue that poverty increases at old age. This is because productivity of the
individual decreases and the individual has few savings to compensate for the decrease of
productivity and income. This is, of course, more likely to be the case in developing countries
where savings are low because of low income and at the old age being mostly dependent. Others
contend that age is correlated with higher productivity and hence impacts welfare positively. A
third view that could be worthy of note to see is that neither of the two approaches be correct.
This is because the relationship between age and poverty might not be linear, as we would expect
that incomes would be low at relatively young age, increases at middle age and then decreases
again. Therefore, according to life theories we would expect to find that poverty is relatively
high at young ages, decreases during middle age and then increases again at old age (Szekely,
1998) in Mekonnen(2002).

52. 39
In Debre Birhan, age of household was not found to be significant in linear terms. The research
classified the age of the household below 20, 20-40, 41-60, and above 60 and the results of the
statistics presented in the appendixes part.
4.2.3 Marital status and poverty
Marital status of the household head is an important constituent of the demographic variables,
has economic implication on household‘s income level. But from different angles there is
positive and vise verse between poverty and marital status of house household head analysis.
Economic theory and most empirical literatures support the notion that the chance of falling into
poverty increases as one is married. This is due to when people get married household size will
increase as new children are born and expenditures increase which in turn leads to searching for
mechanisms of fulfilling additional needs and necessities for the family. On the other hand as
one is married the probability of falling into poverty decreases, as there is more labor forces in
the household and unity. Results indicate that if the household is headed by divorced or
widowed, the probability of falling in to poverty is high compared to household headed by
married. Household that headed by divorced man/woman divides resources into two and limits
the household economic and social efforts from maximizing their welfare status. On other side,
household headed by widowed man/woman potentially may lose the active labor force which
significantly contributes to the household income and consumption.
In this study, from the total of 203 sample respondents, household head with his/her spouse
accounts 68% and the rest 32% are household head without his/her spouse. Of the total
household head without his/her spouse, 73.5% of them are found to be poor; and 26.5% are non
poor. Of the total household head with his/her spouse, only 36% of them are poor and the rest
(64%) are non poor. Cross tabulating the data results in the marital status of the household head
is an important determinant of urban poverty in Debre Birhan.

53. 40
4.2.4 Household family size and urban poverty
The maximum and minimum household size of the study area is 10 and 1, respectively. The
average household size is 4 people per household. An increase in household size has significant
positive influence on the likelihood that a household is chronically poor or fall into poverty trap.
4.2.5 Education and urban poverty
Three types of indicators are normally used to characterize education in an analysis of household
living standards. These include the household members' level of education (literacy rate with
poor households having lower literacy), the availability of educational services (primary and
secondary schools) and the use of these services by the members of poor and non-poor
households (children's registration in school, dropout rate of children by age and gender and
reasons for dropping out, percentage of children who are older than the normal age for their level
of education and average spending on education per child registered). Literacy and schooling are
important indicators of the quality of life in their own right, as well as being key determinants of
poor people's ability to take advantage of income earning opportunities. (Khalid Mahmood
khan).
Education increases the stock of human capital which in turn increases labor productivity and
wages. Since labor is by far the most important asset of the poor, increasing education of the
poor will tend to reduce poverty. The researcher was found that education is negatively related
with urban poverty in the study area Debre Birhan town. We might think of low education as
causes of poverty. The cross tabulation of the survey result showed that households head highest
educational level has a significant effect on the probability of being poor or non- poor. As
household head education level in years of schooling increases the probability of being poor
tends to decrease.
In the study area, the education of the head of the household influences the way the household
relates to the labor market and thus the income-earning opportunities of the household.

54. 41
4.2.6 Employment/occupation and urban poverty
There are several indicators for determining household employment. Within this array of
indicators, economists focus on the rate of participation in the labor force, the real rate of
unemployment, the rate of underemployment and job changes.
Employment has a high and negative relationship with poverty because employment which
requires low amounts of capital, either human or physical can be related with low earnings and
therefore with higher poverty rates. Out of the 203 surveyed households, it is good to get that
156(76.84%) are employed while the rest being unemployed. But most of them were employed
and they couldn't escape from the status of poor. A large number of the household heads whether
below or above the poverty line are engaged in self-employed/self-account works. They account
for 70(34.5%) of the surveyed households. Theoretically, when an individual is employed, the
probability that she /he would fall into poverty decreases. However, kinds of jobs/activities a
household engaged in should be considerable since all activities are not better in return. In most
cases, the self-employed people are the petty traders (Guilt), particularly, left to the women who
in no way can get sufficient income to move out of poverty. In the study area there are many
households who engaged in his own-account businesses into: petty trade (Guilt), trade,
wood/metal work, hotel service, sale of local drinks, sale of foods and handcrafts such as
embroidery and pottery. Only government and nongovernmental organization (NGO) workers
could have potentials to move away from poverty yet their number is insignificant in the town.
4.2.7 Household head Income and urban poverty
The amount of household income at any one time shows the extent of poverty; or household‘s
economic status. Holding other variables constant, there is no need to debate that income directly
or indirectly affect the well /bad being of an individual. It is used as a single most important
proxy of poverty. In the study area the researcher classified flows of income in to four groups
and saw how seems like the economy status of the respondents within their average income. Out
of the total samples 85(41.9%) of them have got less than 1,500 ETB. From thus samples only
20(23.5%) of them were noon poor the rest 65(76.5%) were poor. Under the category of income

55. 42
group1, 500 up to 3,000 there are 59 households account for 29.1 % of the total samples. In this
income group 54.2 % of them were non poor and the rest 45.8% were poor. From this result the
researcher proofs that income is leading factors of household economy status. As household
income increases the probability of being poor is decreases. As expected the researcher income is
negatively related with poverty status. All the rest income groups are similar pattern. See the
4.2.8 Health status/sick member and urban poverty
More than anything else health is the first and single factor for the well/ bad being of individuals.
Without proper health life is difficult. The first question posed in this research was whether any
member of households suffered from disease or not and the alternatives provided to them were
only two: yes or no. From those who respond No 35(27.8%) of them are in the poor category and
the rest 91(72.2%) are in the non-poor category. On the other side, from those who respond yes
62(80.5%) of them are in the poor category and the rest 15(19.5%) are in the non-poor category.
As a result health status is the most significant in the case of Debre Berhan, and it is one of the
determinants for the improvement of poverty as many literatures proved and from theoretical
underpinnings. It is so difficult to think about anything, without physical and mental health.
4.2.9 House, water and Electricity with urban poverty
Housing conditions are important measures of poverty via increased utility and its impact on
health status of households. Population in the study area was very increasing due to rural to
urban migration and natural growth; however, housing services is very poor. In the study area,
some 62.1% of the sample respondents have their own houses. The other 37.9% of the samples
get the housing facility rented from private owners and kebele administrations. The majority of
the poor households who do not own houses live mainly in houses rented from private owners.
From the total households who have their own private house 88(69.8%) households are non poor
the rest 38(30.2%) are poor . on the other hand 18(23.4%) of the total household who haven‘t
their own house are non poor and the rest 59(76.6%) are poor. All about the result indicates that,
house ownership is negatively related to the probability of households to be poor (i.e., house
ownership helps households to come out of poverty. As households own houses the cost that is to

56. 43
be incurred as a house rent becomes saved; and the house itself can be used by the households
could be taken as a productive asset.
It is obvious that, wellbeing resides in living and getting life sustaining goods and services. One
of these elements is access to pure water which is basic for the health and smooth functioning of
one‘s body. But in many cases, the poor have no access to such service. Access to pure water is
mostly related to the source. A large population does have access to clean drinking water and
water is not the problem in Debre berhan town. Almost all(85.6%) of the sampled households
have their own private piped water. Even though, some of the households haven‘t their own
private tape water service the probability of being poor is not much differ than who have. And
water is not significant factor in case of Debre Berhan town.
Access to electricity from a generator or line connection rises sharply with income. Of the total
sample respondents, 70.4% have their own electrometers; while the remaining 29.6% do not
have such facility. From the total households who do not have their own electrometers, 71.6 % of
them were poor. While the rest 28.4% of them were non poor. From the total households who
have their own electrometers, 37.7 % of them were poor. While the rest 62.3% of them were non
poor. Though many households have not their own electrometers, they use electricity from home
light rented from neighbors. Even though households have their own electro meter some
households do not have to use cooking. Almost all households have use electricity only lighting
purpose. No doubt that electricity is significant factor of determining urban poverty.
4.3Econometric Analysis of the Results, Diagnostic tests
Before going to estimate the specified model, it is important to undertake different tests on
whether the basic assumptions of the model are met or not. In addition, the goodness of fit of the
model should also be tested. Hence, the tests will be as follows.

57. 44
4.3.1 Test for multi-collinearity
Households' characteristics of explanatory variables need to be tested for the estimation of
logistic regression model to identify those variables that are determinants for status of a
household poverty. To estimate Binary logit it was first important to compute the hypothesized
variables have any associations or correlation one on another using variance inflation factor
(VIF) and contingency coefficient (C). Variables were first tested to check the multicollinearity
effect or any associations between continuous explanatory variables.
The computed VIF values in Table below are less than one that confirms there is no serious
problem for the multicollnearity among the continuous variables. This further ensures that there
is no variable that whose value is 10 and greater. Thus the variable at hand will be used and
entered for running of the Binary logistic regression.
When there is collinearity among variables, 𝑅2 approaches one; while 1/VIF approaches to zero.
When there is no multicollinearity, 𝑅2 approaches zero; while 1/VIF approaches one. As a rule
of thumb if a VIF of a variable exceeds 10, the variable is said to be highly collinear with
explanatory variables. This case, 1/VIF value would be slightly far away from zero; and it
approaches one. In additi on the mean value of VIF becomes 1.33. This implies that, there is less
and acceptable collinearity because the role of the game is 10. Since this result is less than 10
there is no multicollinearity problem.
4.3.2 Test for Hetroscedasticity
What heteroskedasticity is? Recall that OLS makes the assumption that V (εj) = σ2
for all j. That
is, the variance of the error term is constant. (Homoskedasticity). If the error terms do not have
constant variance, they are said to be heteroskedastic. The term means ―differing variance‖ and
comes from the Greek ―hetero‖ ('different') and ―skedasis‖ ('dispersion')( Wikipedia,2015).
Hetroscedasticity means a situation in which the variance of dependent variable in this study the
probability of being poor or non-poor/ varies across the data. Hetroscedasticity complicates