Arba Minch University School of Graduate Studies

ARBA MINCH UNIVERSITY SCHOOL OF GRADUATE STUDIES

THE EFFECT OF INFORMAL BUSINESS PARTICIPATION ON URBAN POVERTY REDUCTION: IN ARBA MINCH TOWN OF, ETHIOPIA

BENYAM TASSEW

January, 2018

ARBA MINCH

The Effect of Informal Business Participation on Urban Poverty: Case of Arba Minch Town, Ethiopia

A Thesis Submitted to College of Business and Economics Department of Economics, School of Graduate Studies

ARBAMINCH UNIVERSITY

IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF MASTER OF SCIENCE IN DEVELOPMENT ECONOMICS

Benyam Tassew Tadesse

January, 2017

ARBA MINCH UNIVERSITY

ACKNOWLEDGEMENTS Above all, I would like to forward my deepest gratitude to my almighty God and his mother, St., Virgin Merry, for helping me to accomplish my will. No word of thanks and gratitude is sufficient to appreciate them have done for me. By their decree, this paper came out as a result of the contribution and support of many individuals whom I am greatly indebted to.

I really want to express my greatest thanks to my advisor Dr. Sisay Debebe for his patience and constructive advice throughout the development of this thesis without which this paper would have been lost. Besides, my special gratitude goes to my Co-advisor Mr. Tilahun Habte for his remarkable advices and encouragements throughout the course of the study.

I would like to warmly acknowledge my families, for its remarkable encouragements throughout the course of the study. I want to extend my deepest gratitude to Chamo, Doyisa, Gurba and Dilfana sub-city administrative office and Arba Minch town Administration for material support.

My thanks also go to the institutions, Commercial Bank of Ethiopia Secha Branch, Arba Minch University Department of Economics for their remarkable encouragements throughout the course of the study and unconditional cooperation in every aspect of my thesis work. And I want to extend my deepest gratitude to Chamo, Doyisa, Gurba and Dilfana sub-city administrative office and Arba Minch Town Administration for material support.

Last, but not least, my heart-felt appreciation goes to all my beloved families for their endless help and valuable advises, my friends who have encouraged, collaborated and shared me their experiences for the completion of this study and my class mates for their support and motivation.

iii

ACRONYMS AND ABBREVIATIONS

AE Adult equivalent

ATT Average treatment effect on treated

CIA conditional independence assumption

GNP Gross National Product

UNDP United Nations Development Program

WMS Welfare Monitoring System

HICES Household Income, Consumption and Expenditure Survey

CSA Central Statistical Agency

MoFED Ministry of Finance and Economic Development

PASDEP Plan for Accelerated and Sustained Development to End Poverty

ILO International Labor organization

UNDP United Nations Development Program

MoLSA Ministry of Labor and Social Affairs

UNESC United Nations Economic and Social Council

UNECA United Nation Economic Commission for Africa

UN United Nation

SNNPR South Nation Nationality People Region

SPSS Statistical Package for Social Science

MPAEHI monthly per adult equivalent household income

TABLE OF CONTENTS LIST OF FIGURES ………………………………………………………………………….…..i LIST OF TABLES ………………………………………………………………………….…...ii ACRONYMS ……………………………….…………….……………………………….…....iii 1. CHAPTERONE: INTRODUCTION ……………………………….………..………...…1 1.1 Background of the Study ……………………………………………………………….1 1.2 Statement of the Problem ……………………………………………………………….3 1.3 Research Question………………………..………………………………………...... 4 1.4 Objective of the study... …………….…………………………………………………..5 1.5 Significant of the study ………………………..…………………………….………….5 1.6 The Scope and limitation of the Study…………………………………….…….………6 1.7 Organization of the study ……………………………………………………………….6

2. CHAPTER TWO: LITERATURE REVIEW…………………..……………………….....7 2.1 Theoretical Literature Review ……………….…………………………...…………….7 2.1.1 Definitions and characteristics of informal sector………………………………..7 2.1.2 General Characteristics of informal sector ……………………………………….8 2.1.3 Definition of poverty and Measurement …………………………………………9 2.1.4 Measures and Indicators of Poverty ……………………………………….……11

2.2 Empirical Literature Review………………………………………………………...…12 2.2.1 Status of Urban Poverty in Ethiopia …………………………...…………....….12 2.2.2 The Informal Sector in Ethiopia Emergence and Expansion ………………...…14

2.3 Conceptual framework of the study …...... 16

3. CHAPTER THREE: RESEARCH METHODOLOGY………………….…...... ………..19 3.1 Description of the study Area………………………………………………………….19 3.2 Data Type and Methods of Data collection …………..………………………...…..…21 3.2.1 Sampling size Determination and Techniques………………………………….21 3.2.1.1 Sampling size Determination …………………………………………...…....21 3.2.1.2 Sampling Techniques ………………………………………………………..22

3.3 Methods of Data Analysis……………………………………………….………....….23 3.3.1 Descriptive Statistics ……………………………………………………….…...23

3.3.2 Econometrics Model ……………..…………………………………..…………24 3.3.2.1 Propensity Score Matching Method……………….………………………….24 3.3.2.2 Mathematical Specification of PSM Method ………………………………...24 3.3.2.3 Implementation of the Propensity Score Matching…………………………...29 3.3.2.4 Sensitivity analysis……………………………………………………………34 3.4 Measurement of Poverty……………………………………………………………….37 3.4.1 Food Poverty Line……………………………………………………………....37 3.4.2 Non-Food Poverty Line…………………………………………………………38 3.4.3 Measurement of poverty …………………………….………………………….38 3.4.3.1 The incidence of poverty (headcount index) ………………………………....39 3.4.3.2 Poverty gap index……………………………………………………….…….39 3.4.3.3 Severity of poverty (which is squared poverty gap index) ………………..…40

3.5 Definition of Variable and their expected Hypothesis ………..………………..….….40 3.5.1 Outcome variables ………………………………….…………………..……….40 3.5.2 Independent Variables ……………………………………………………….… 41 4. CHAPTER FOUR: RESULTS AND DISCUSSION……………………..……………...47 4.1 Descriptive results ………………………………………………………………….….47 4.2 Description of informal business sector in Arba Minch……………………………….47 4.3 Demographic and socio-economic characteristics of sample households …………….48 4.4 Measurement of poverty ……………………………………………………………....50 4.4.1 Extent of poverty in Study Area …………...…………...………………………51 4.4.1.1 Incidence of Poverty or Headcount Index ……………………………………52 4.4.1.2 Depth of poverty or Poverty Gap Index…………...………………………….53 4.4.1.3 Poverty severity or squared poverty gap …………………………………..…53 4.5 Impact of informal sector on the poverty of participants ……………………………...53 4.5.1 Consumption expenditure of participant ………………………...……………...53 4.6 Empirical Results of Econometric Estimation ………………………………………...54 4.6.1 Choice of matching algorithm…………………………………………………...55 4.6.2 Imposing common support condition ………………………………………...... 56 4.6.3 Testing overlap and conditional independence assumptions……………………..57 4.6.4 Testing the matching quality…………………………………………………….57 4.6.5 Estimating Treatment Effect on the Treated …………………………...……….59 4.6.5.1 Effect on informal business sector on consumption expenditure……………..59 4.6.5.2 ATT estimation of impact of using informal sector on household income…...61 4.7 Robust Test ……………………...…………………………………………………….62 4.8 Econometric Analysis………………………………………………………………….63 4.8.1 Overall Fitness of the Model…………………………………………………….63 4.9 Estimation of propensity scores………………………………………………………..64 4.10 Sensitivity Analysis of Result………………………………………………..…67

5 CHAPTER FIVE: SUMMARY, CONCLUSION AND RECOMMENDATIONS ……...….69

5.1 Summary ………………………………………………………………………………….70 5.2 Conclusion ……………………………………………………………………..………….72 5.3 Recommendations ……………………………………………..…………………………..73

6 References ……………………………………………………...………………….…...I 7 Appendix ………………………………………………………………………………..IV

vii

LIST OF DIAGRAM

Page

Dia-1 Conceptual Framework of the study ……………………………………………………...17

viii

LIST OF FIGURE

Page

Fig-1 Map of the study Area………………………………………………………….………..20

LIST OF TABLES

Page

Table 3.1: Proportionate sample size of household by their kebeles ………………..……….….23

Table 3.2 Description of the variables, measurement and their expected hypothesized…..….…46 Table 4.3: Distribution of sample households for continuous variables………………………....48 Table 4.4: Distribution of sample households for dummies variables …………………….…....49

Table 4.5: Extent of Poverty in Arba Minch Town informal sector Participant……….………..52

Table 4.6: Differential impact on consumption expenditure…………………………………….54 Table 4.7: Distribution of estimated propensity scores ………………………………………….57

Table 4.8: Chi-square test for the joint significance of variables ………………………..………58 Table 4.9: ATT for consumption expenditure …………………………………………..……....61

Table: 4.10 ATT estimation of impact of informal sector participation on food consumption….61

Table: 4.11 ATT estimation of impact of informal on non-food consumption …………...…….61 Table 4.12: ATT estimation of impacts of informal sector on household income ………..…….62

Table 4.13 Bootstrap standard error …………………………………………………………..62

Table 4.14: Logit model results of household program participation……………………….…...65 Table 4.15: Sensitivity analysis ……………………………………………………………...…68

ABSTRACT

Informal sector has been found to be a critical instrument for reducing the poverty in urban people. It is prominently used to improve the livelihood of urban households where it is believed to be underexploited in research and hence is indispensible to examine its real effectiveness. To this end, this study aims to assess the effect of informal sector on urban poverty reduction. A cross-sectional survey data collected from 235 urban households (96 participant and 139 non- participant) using multi-stage sampling techniques from Arba Minch Town. Both Descriptive statistics and econometric model were applied for analysing quantitative data. PSM method was employed to analyse the impacts. The result of the study showed that level of education and family size of households were found to have negative relationship whereas age of the household head and access to remittance were found to have positive contribution to poverty reduction. Moreover results obtained from PSM show that participation in informal sector has a significant, positive and robust impact on the outcome variable measured using household consumption expenditure. The sensitivity analysis also shows that the impact result estimates are insensitive to unobserved selection bias and independent to the chosen matching estimators. Based on the result this study recommended that it is better off the government make some policy and regulation for this sector in order to prevent the formal sector from unwanted competition and Government should constrict strategies for informal sector to enhance to transform to formal sector.

Key words: Informal Sector, poverty, Impact, Propensity Score Matching, Arba Minch

1 INTRODUCTION

1.1 Background of the Study

The informal sector represents an important part of the economy and certainly of the labour market in many countries especially developing countries, and thus plays a major role in employment creation, production and income generation. In countries with high rates of population growth and/or urbanization, the informal sector tends to absorb most of the growing labour force in the urban areas (Farhad, 2013).

Informal sector is capable of absorbing large proportion of the new entrants into the labor force that the formal sector is unable to cope with the increasing numbers of the poor, unskilled, and illiterate. The majority of survival needs drivers such majority to create employment or self- employment and generate income in the informal sector. The sector provides employment more over necessary goods and services for the lower income groups ((ILO), 2012).

It operates as a means to access paid work where this might be difficult in the formal sphere, a situation that affects diverse groups such as people with poor educational or vocational qualifications, those who have been out of work for a period of time. Informal paid work can have a positive role in peoples‘ lives, keeping them from poverty, and the development of confidence and skills, and building social capital (Tipple.A.G., 2005).

The sector plays important role of income distribution, make active competition, exploit market functions, improve productivity and technical change and finally creates economic development.

According to Llanes et al. M and Barbour A, Hatcher M, (2007),Copisarow R and Barbour A, (2004), Neale, E. and Wickramage A,(2006). The positive consequences of the informal economy are that it: Increases income and Increases self-confidence, Improves skills, Expand work experience, Develops the habit of work, Leads to minimize cost of product, sustain economic activity, provides employment, Offers flexible working hours and conditions, Has reduced barriers to entry, promotes entrepreneurial spirit, Supports the formal economy.

It minimizes costs of customers due to minimize their cost by absence of taxes, social security contribution, and obligatory deduction and work regulations. (Maliyamkono and Bagachwa, 1986).cited in Sisay Seifu, (2005).

The importance of informal sector is providing income and employment many of who cannot get employment in the formal sector. The urban informal sector plays greater role in the economies of developing countries. In developing countries, an half to three quarter of the non-agricultural labour force is in the informal sector. (Martha et al.Alter Chen and Marilyn Carr, 2001).

In Ethiopia, as indicated in a document produced by Ministry of Labor and Social Affairs (MoLSA) entitled: Labor Market Dynamics in Ethiopia (2013), of the shares of informal economy employment for the years 1999 -2010 had the proportion of working population in the informal sector with a significant decline from 72.8% in 1999 to 38.3% in 2010 (Affairs, 2013).Out of the total employed population in urban areas of the country, 40.1% were engaged in the informal economy. The highest percentage share who were working in the informal economy was found in Somali region (46.5%) followed by Gambella region (42.1%). The lowest proportion of people engaged in the informal economy was found in Addis Ababa City Administration (20.5%).The sector also provided most of the population with a means of livelihood or essential supplementary income. Most probably the sector is also the only reliable source of livelihood for women and the poor, for whom the formal sector has no accommodation for economic engagement (MoLSA, 2013).

In some countries including Ethiopia, there are more women than men in informal employment, even in absolute number. The informal sector includes activities and works that are less visible and, even, invisible. Less visible informal workers work in small shops and workshops. On the street corners of most cities, towns, or villages, even in residential areas, are countless small kiosks or stalls that sell goods of every conceivable kind (ILO, 2002).

As any other developing countries of the world and being one of the least developed countries, one of the serious concerns of all urban centers in Ethiopia is the extent of urban poverty and unemployment experience. In order to cope with the problem, the poor involve in prostitution, begging, borrowing, migration, and change in consumption pattern. The poor also engage in the urban informal sector to make their living as any other developing countries of the world (Tegegne, 2000). Cited in Amene Afework, in 2011).

The purpose of this study is to identify the effect of informal business in urban poverty in Arba Minch town in relation to their business activities, to describe the degree of challenges they faced

2 in relation to their work, to examine the income level generated from their business activities and to assess their efforts and contributions towards the development endeavor of their household.

1.2 Statement of the problem

Informal sector as a means of urban air pollution and then case of sick on neighborhoods And also the cause of increase the formal sector payment for pollution imitation tax cause they create pollution higher than formal sector due to nature of activity on the other hand non-taxed at all. ( (Sarbajit, 2006))

Informal sector cannot easily control. This can lead to illegal or unsafe activities that mean no guaranty for health and safety during on production process, storage and selling. And also lead to leas quality and short expiry. The sector direct negatively affect social benefits. it entails a loss in budget revenues by reducing taxes and social security contributions paid and therefore the availability of funds to improve infrastructure and other public goods and services. It invariably leads to a high tax burden on registered labour.

Scientific studies on the challenges and perspectives of informal sector in urban poverty and their coping measures are still in short supply in Arba Minch Town. Several studies on informal sector development have been conducted by various professionals and researchers in different countries for different purposes. For instance, Selamawit (2008) conducted a study on the participation of women in the urban informal sector with particular focus on petty traders in Sebeta town basically to know the factors that pushed/attracted women to informality, to see the contribution of earnings to the family, and to examine the nature of the business and its major determinant. She found out that lack of job opportunities, heavy family responsibility and the need for additional income were the factors behind women participation into petty trade.

Etsubdink 2011 has also conducted a research Cause and Effect of Informal Sector: the case of Street vendors in Addis Ababa, Ethiopia. She found that seeking to create employment and unable to fulfil requirement of formal sector are the main reasons that street vendors to become in informal sector in Addis Ababa.

Asmamaw, 2004 has condacted a research Some Controversies On Informal Sector Operation in Ethiopia: Problems and Prospects for a Development Strategy he found that the contribution of the informal sector, is the transaction of from informal sector to formal sector and also alleviating urban poverty through creating jobs and by reducing unemployment.

The research gap, here is methodology, methodology of the above all researches are descriptive and some researcher‘s uses logit model However, the contribution of the above researcher‘s are made in efforts to create awareness and bring about change of attitude on the part of governments, non-government and other stakeholders.

Few researches were conducted in other Town but their scope was limited to a specific operator group on specific issue of the informal sector.

Ever since the recognition of the informal sector as a socio-economic force, the debate on the importance of the sector in socio-economic development and urban poverty has continued to date among government authorities, formal sector operators, the community and the informal actors themselves. As a result, governments are trying to consider the sector as the fundamental base for entrepreneurship development and effective utilization of scarce resources. In this regard, various studies have been made to accommodate issues of the informal business particularly and the sector generally.

It is therefore difficult for officials and policy makers to provide pragmatic solutions to the urban poverty situation in the sector. In order to provide objective solution to the urban poverty situation in the sector, this study will fill in the gap by conducting a study on the effect of informal business participation in urban poverty and the various problems faced by informal business participant at the Town.

1.3 Research Question

The basic Question in this study is:

 What are the main factors that influence household to participate on informal business?  What is the effect of participation on informal sector on urban poverty in the study area?

1.4 Objective of the study

The main objective of the study is to examine the determinants of household participation on informal business and its effects on urban poverty in Arba Minch Town. The Specific objectives of the study are:

 To describe the characteristics of informal business participant and non-participant households in the study area.  To measure the status, extent and depth of poverty among informal business participant and non-participant households.  To examine the effect of informal business practice on urban poverty.  To evaluate the factors that affect households participation on informal business in Arba Minch town  To assess Challenges of informal business in the town.

1.5 Significant of the Study

The results of study are important in summarizing the effect of informal business on urban poverty redaction it will give practical insight on the general living condition of participant in the urban informal sector, there by the challenges they have been facing. Thus, enable the concerned bodies to play their role in enhancing and understanding the living condition of operators as well as it is important to fill the knowledge gap existed on the role of informal sector activities has in generating income and creating employment opportunities and The findings of the study will create awareness as to what policies and strategies the local government and national government should develop in order to increase the contribution of the sector to the development of the operators themselves and to the town as well as to the national Level. However, this study will serve as first-hand information or a stepping stone for other researchers interested in related topics. Finally, this study will significantly help in sharing the experiences of informal sector business to other similar Towns and also for government policy as well as for non-government organization.

1.6 The Scope and limitation of Study

This study is mainly concentrates to assess the effect of informal sector business on urban poverty in Arba Minch Town. This study only limited on the informal business activities which are found in Arba Minch town in four kifele Ketemas.

The informal sector business covers a wide range of activities. Some of the activities includes, street vendors, hawkers, home-based workers, cobblers, porters, labourers, artisans, selling fruits and vegetables, clothes and shoes, various items in kiosk, food processing and sale, small manufacturing, small traders, production, construction and repair of goods, coolies, money changing, domestic works, prostitution, drug peddling, small-scale artisans, barberry and shoeshine (UNESC 2006).

However, in this study, only eight informal sector activities are selected, namely: street vendors, selling cooked foods/drinks, selling clothes/shoes, bicycle/motor repairing and renting, vegetable/fruit vending, beauty work, shoes polishing and brokers are selected. Because of some Limitation like financial, time, experience of the researchers and the like.

1.7 Organization of the study

This thesis is organized into five chapters. The first chapter deals with the introductory part, comprising the background, statement of the problem, objectives, significance, and scope and limitation of the study. The second chapter has intensely reviewed the available literature by entailing general concepts of informal sector and empirical research results executed elsewhere. The third chapter gives a full emphasis of the methodology employed for the thesis, including description of the study area, sources and methods of data collection, sampling procedure and the analytical technique employed in the analysis. The fourth chapter discerns the credential of the survey results by discussing it in comparison with the results of other studies. The final chapter summarizes the findings of the study and provides some recommendations about informal sector in study area.

2. LITERATURE REVIEW

2.1 Theoretical Literature Review

2.1.1 Definitions and characteristics of informal sector

Informal sector is ''Sector which encompasses all jobs which are not recognized as normal income source which taxes are not paid. The term is sometimes used to refer to only illegal activity, such as an individual earn wages but does not claim them on his or her income tax, or a cruel situation where people are forced to work without pay. However, the informal sector could also be interpreted to include legal activities such as jobs that are performed in exchange for something other than money Opposite of formal sector.''

The informal sector or informal economy refers to activities and income that are partially or fully outside government regulation, taxation, and observation. The main attraction of the undeclared economy is financial. The activity allows employers, paid employees, and the self-employed to increase their take-home earnings or reduce their costs by escaping taxation and social contributions. It is means of employment who cannot find a job in the formal sector. But, a loss in budget revenues by reducing taxes.

Definition of informal sector different in different school of thought and have a lot of definitions in different researchers. So that it is difficult to get one definition on the informal sector because of heterogeneity of nature of the activity however it was widely defined as unregulated economic enterprises

For simplification it is better to give definition for informal sector based on characteristics, The known definition given by ILO that the way of the activity characterized by easy to entry mean that not need much training, education and capital. , depend on local resources; family ownership of enterprises; small scale of operation; labour-intensive, skills acquired outside the formal school system; and not officially regulated and competitive markets.

The basic nature of the informal sector units can be summarized have little or no division between labour and capital, self-employed activities with the help of unpaid family members or a few hired workers with low wage than formal sector and without guarantees mean wage level and working condition is unprotected, consists of small scale, at a low level of organization and

7 technology with the primary objective of as means of employment rather growing organization (Etsubdink 2011)

2.1.2 General Characteristics of informal sector

The informal sector is characterized by a large number of small-scale production and service activities that are individually or family owned and uses labor-intensive and simple technology (Todaro and Stephen, 2003).

Easy of entry, reliance on indigenous resources, family ownership of enterprises or activity operated by the owner with few or no employees., small scale of operation, labour incentive and adaptive technology, skills acquired outside the normal school system, have little or no access to organized markets, to credit institutions, unregulated and competitive markets. (ILO, 2003).

To start with operation in the informal sector depending on its scale of operation doesn‘t require formal education, procedures and other requirements. Studies covering twenty one African countries show that only a quarter of enterprise in the informal sector acquire their skills from formal school and training centers.(ILO,2003).

"Small-scale activities characterized by self- employment, mainly using self-labor and household laborers (usually less than ten), simple technology, low level of organization and unfixed operation of premises and working hours.‘‘(ILO, 2003)

According to CSA urban informal sector survey of 2003 has mainly engaged in marketed production, not registered as companies or co-operatives, no full written book of accounts, less than ten persons engaged inactivity, no license & fixes time of operation, small-Scale operation and usually uses indigenous, local raw materials.

We can categorize characteristics of informal sector based on employment that the people engaged in the informal sector and enterprise that the activities in the informal sector.

Characteristics of the people engaged in the informal sector

 Absence of official protection and recognition

 Non coverage by minimum wage legislation and social security system

 Predominance of own-account and self-employment work

 Absence of trade union organization

 Low income and wages

 Little job security

 No fringe benefits from institutional sources

Characteristics of the activities in the informal sector

 Unregulated and competitive markets

 Small scale operation with individual or family ownership

 Ease of entry

 Reliance on locally available resources

 Family ownership of enterprises

 Labor intensive and adapted technology

 Absence of access to institutional credit or other supports and protections

2.1.3 Definition of poverty and Measurement

There is no unanimously accepted definition of poverty. As a matter of fact it is almost never defined in itself, but through other concepts, such as growth, well-being, exclusion or equity. A basic feature of the concept of poverty is its complex and multidimensional nature which makes the plurality of definitions is inescapable.

According to English dictionary, the word ‗poverty‘ refers to the state of being very poor. The dictionary meaning is a general definition, lacking in specificity contextually. There is ambiguity as to the sense in which poverty is expressed. The word can be understood to mean the whole gamut of deprivations which may be economic, social, spiritual, political, cultural or in fact environmental. Clarity in terminology in this regard requires the need to make some distinction.

Distinction can be made between material and non-material deprivation. Poverty in the sense of non-material deprivation relates more to the spiritual aspect of man rather than the physical being. For instance, poverty in the form of spiritual deprivation is a reflection of the debased

9 human nature and the corruption of the human heart. This is a case of spiritual poverty; it manifests itself in greed, slavery and general powerlessness over the grip of sins.

Material deprivation has always been the emphasized and this relates to, on one hand, lack of physical necessities, assets and income. And, on the other hand, it has to do with relate to the general condition of deprivation such as social exclusion, vulnerability, lack of access to productive resources and basic social services and so on. In a more operational sense, material deprivation can be categorized into income poverty and human poverty. The former is understood as living with low income, low consumption, poor nutrition and poor living conditions. Human poverty, on the other hand, describes the conditions of low health and low education. Where as the dichotomy of income and human poverty is needful to achieve operational objectives and for the purpose of appreciating action points for poverty eradication, the two are nonetheless not really mutually exclusive. Income poverty, in most cases, is associated with the so-called human poverty in a vicious circular manner. As a matter of fact both culminate in social deprivations, namely high vulnerability to adverse events such as diseases, economic crisis or natural disaster, voicelessness in the society and powerlessness to improve living circumstances.

The multidimensionality of poverty has been stressed and succinctly expressed in the Copenhagen Declaration on Social Development in the following manner:

―Poverty has various manifestations, including

 lack of income and productive resources sufficient to ensure sustainable livelihoods;  hunger and malnutrition;  ill health;  limited or lack of access to education and other basic services;  increased morbidity and mortality from illness  homelessness and inadequate housing;  unsafe environments; and social discrimination and exclusion  Lack of participation and exclusion.  Lack of participation in decision-making and in civil, social and cultural life‖. [World Summit for Social Development, Copenhagen, 1995]

Apparently, the multidimensional nature of poverty is what has given rise to the plurality of definitions and for now; the conceptualization of definition of poverty is still in progress.

2.1.4 Measures and Indicators of Poverty

Economists have differed as to whether poverty should be measured in absolute sense, defining poverty as people falling below some fixed minimum income or consumption level; or whether it should be defined in relative terms, so that poverty means inability to afford what average people have. If an absolute measure is accepted, it is at least conceivable to have everybody lifted above the poverty line whereas if poverty is measured in relative sense, some people will at least fall below the so called poverty line, which means the poor will always be with us.

Poverty line is a basic measure and an instrument for identifying and measuring income poverty. It is defined as an arbitrary income measure, usually expressed in constant dollars (e.g. $2 per day), used as a basis for estimating the proportion of a country‘s population that exists at bare level of subsistence. Based on household income or consumption, poverty lines quantify absolute poverty in monetary terms and characterize people in terms of their monetary income or consumption, particularly of food. Thus, a poverty line is just a cut-off line (or threshold) used to distinguish between ―poor‖ and ―non-poor‖ households

Setting a poverty line permits the calculation of the following poverty indicators

 Poverty rate or incidence of poverty  Depth of poverty or poverty gap  Severity of poverty One final measure of poverty, credited to the United Nation Development Programme (UNDP) in its 1997 Human Development Report, was introduced against the background of dissatisfaction with the 2 dollar per day World Bank income measures. As articulated in the Report.

―Poverty has many faces. It is much more than low income. It also reflects poor health and education, deprivation in knowledge and communication, in ability to exercise human and political rights and absence of dignity confidence and self-respect‖ [UNDP, Human

Development Report, 1997]. Hence, in place of the World Bank‘s notion of income poverty, the UNDP developed a measure of human poverty – that is, human poverty index (HPI). The HPI constructed is a multidimensional measure of poverty, incorporating three key deprivations in respect of survival, knowledge, and economic provisions. The deprivation in longevity (survival) is measured as the percentage of people not expected to survive to age 40, and the deprivation in knowledge is measured by the percentage of adults who are illiterate. The third deprivation, economic provisions, relates to a decent living standard. It is represented by a composite of three variables, namely the percentage of people without access to safe water, the percentage of people without access to health services, and the percentage of malnourished children under five.

The measure, HPI, provides a quantitative and more comprehensive poverty indicator when compared to income poverty index. Income poverty, no doubt, needs to be measured, but income alone is too narrow a measure. Thus, HPI developed by UNDP, provides a more robust and broad measure of poverty indicator, summarizing the extent of poverty along several dimensions. The index makes possible a ranking in relation to a combination of basic deprivations and also serves as a useful complement to other measures of poverty and human deprivation-including income poverty. A shortcoming of HPI, however, is that it is somehow aggregative as it is not possible to associate the poverty incidence with a specific group of people or number of people.

2.2 Empirical Literature Review

2.2.1 Status of Urban Poverty in Ethiopia

Urban poverty situations are usually linked to the problems associated with urbanization, which in turn is linked to the massive movement of people from the rural areas to the cities. As observed by Sabry (2010), given the massive movement of people from the rural areas to the cities, the scale of urban poverty has been on the rise worldwide, thus creating urban slums, often referred to as informal settlements, which are areas without enough resources, with degraded environmental conditions, without or with limited access to proper sanitation, clean water, electricity and health care facilities. These worsening environmental conditions in turn damage residents‗ health and entrench the stigma and isolation of living in informal settlements, making it all the more difficult to escape from poverty (Montgomery and Hewett, 2004) .

Poverty lessening tools and approaches developed for rural areas will not work in urban areas, for the reason that urban poverty is different in its nature from rural counterparts (UNDP, 2007).Urban sectors share of the poor population in a developing country increases its share of the total population. As the dimensions of poverty are many, there are subsets of characteristics of urban poverty which are more pronounced and require specific analysis (Mboup, 2003).

At the same time, as a result of rural to urban migration, the number of poor in urban area will rise in developing countries (Todaro, 2004). Baker and Shuler (2004) outlined clear-cut and indispensable characteristics of urban poverty that is quite different from their rural areas i.e.:

 Commoditization or reliance on cash economy for food, fuel, housing and are often more expensive in town than in rural areas. More than 60% income of urban poor spends for consumption on agricultural products and more suffer from higher food prices.  Overcrowding living condition (slums).While towns become more modern, the growth of slums lead to pollution problems, unemployment, deficiency in basic services, and food insecurity issues. For that end, World Bank launches antipoverty projects to cope up the problems.  Environmental hazards derive from stupidity and perilous location of settlement and expose to multiple pollutants. Crime and violence are highly pervasiveness in urban than rural. The study by Sabry (2010) indicates that the poverty estimates in the Egyptian urban areas in 2009 was 11 percent. In Cairo, especially the Greater Cairo made up of Giza, Qalyoubia and Helwan areas, the poverty rate is high because they form parts of the slums/informal settlements in the city. The study also revealed that these areas have the highest rate of malnutrition, where about 16 percent of children were underweight which is much higher than the recorded income poverty rate in urban areas. The study also revealed that the costs of basic non-food needs—such as for housing, transportation, basic education and health, and access to water, sanitation and electricity—are much higher than commonly recognized.

In Ethiopian, according to an official document entitled ‗Development and Poverty in Ethiopia 1995/96-2010/11‗ (MoFED, 2013) stated that Urban poverty declined substantially between 2004/05 and 2010/11, but only limited the incidence and depth of poverty. The 2010/11 urban poverty head count and poverty gap are lower than that of 2004/05 by 27% and 10%,

13 respectively, and poverty severity of 2010/11 is higher than that of 2004/05 by 5%. The changes of poverty incidence are all statistically significant. The decline in urban poverty incidence and gap could be attributed to the pro-poor activities undertaken in urban areas since 2005 including the on-going efforts waged by the government to creating favorable environment for private sector investment, promote micro and small enterprises development, job creations and distribution of subsidized basic food items provided to the urban poor in times of inflation over the last five years. However, in urban areas too, the growth fails to significantly reach the bottom poor as these extreme poor people are unable to cope with the inflation (MoFED, 2013).

2.2.2 The Informal Sector in Ethiopia Emergence and Expansion

Like any developing socio-economic environment, the majority of Ethiopia‘s population (60%) lives in a state of absolute poverty. Ethiopia is characterized by low level of economic growth, rapid population expansion, drought, famine and rural to urban migration, which has been experienced over the last couple of decades. The per capita GNP was about USD 110 in 1992 with an average growth rate of -1.9% since 1980 (Hayat Abdulahi, 2000, as quoted from WDR 1994). The poor performance of the economy has resulted in rising unemployment and decline of real wages. Growth of population and labor force due to rural-urban migration has inflated the urban workforce. Accordingly, lack of capacity on the part of the formal sector to absorb the growth in population forced the unemployed to seek refuge in the informal sector in order to create own employment (Street Business Operators Task Force/Addis Ababa). This resulted in concentration of labor force in micro-income generating activities urban areas as a natural desire for survival on the part of destitute men, women and the youth. Studies have shown that in many poor countries, like Ethiopia, heavy burden of taxes, corruption and bureaucratic intricacy have driven formal actors into the informal sector (Azuma & Grossman, 2002). The structural adjustment programs, the various austerity measures and the proliferation of economic activities outside of the formal structure have made the economically active labor force to align itself with the informal sector (Haeri, n.d,).cited by (Asmamaw Enquobahrie (ph.D)).

Another factor that helped in the expansion of the informal economy is the reaction against government‘s regulation of the economy. The taxation systems, social legislation, health and environmental controls that are imposed on the activities of the business community and the

14 economic hardship during periods of economic recession forced business people to go informal to operate outside of the regulatory framework (Portes, 2002).

Because of cheaper imports and illegal inflow of commodities from outside, the labor - intensive manufacturers of consumer goods could not remain competitive in the market and were forced to close or move underground. In addition, the industrialization process that took place under unacceptable social and economic conditions that imposed standards set by governments also pushed formal enterprises to use informal means to obtain comparative advantage relative to the more regulated areas of the economy.

The diminishing supply of rural lands and population explosion and resource scarcity, could not enable rural people to sustain life in their localities. The widening gap between the resource- loaded urban centers and the poverty-ridden rural areas facilitated migration as an option for survival (Addis Ababa City Administration.). For example, the 2.9% increase in the population of Addis Ababa is found to be a result of rural-urban migration which has escalated the rate of unemployment in the city.

According to the 1994/95 survey, it is indicated that 46.7% were unemployed. From among the economically active population of 10 years of age and above 61% was found to be in the informal sector (Central Statistical Authority/Ministry of Labor & Social Affairs - Ethiopia, 1997).

Because of the labor intensive nature, use of local raw materials and more simple and flexible technology and the innovative tendencies of informal activities, the actors operate in response to market forces and are skilled in taking advantage of their close links with grassroots communities and institutions. The ease of entry and exit opportunities enables them to be more flexible in coping with the dynamic socio-economic environment.

Although the informal sector exists as a natural ally of the formal sector, it has remained neglected and has not been integrated as useful partner in the development process. Such a situation has created a hostile environment resulting in uncertainties to undertake risk-ridden business activities which threatened the income earning and productive potentials of the sector.

Despite the economic crisis and the problems of the structural adjustment programs that have adversely affected the economic development of poor countries, the informal sector has survived and supported a significant proportion of the impoverished population through employment creation to provide the means for their livelihood.

In the Ethiopian situation, like other developing countries, the informal sector has come about as a result of the socio-economic crisis created by local and external forces and as a response to the search for a means of earning a modest living (UNECA, October 1993). Its expansion and development is, thus, determined by the worsening socio-economic crisis and the creation of enabling environments to accommodate the needs and requirements of the sector.

In Ethiopia, the informal sector has currently become a priority issue of concern by the government, in particular the Addis Ababa City Administration where the majority of informal actors in the country exist. Accordingly, all efforts are being exerted to assist informal sector operators to get organized in preparation for a relatively formal business undertaking which will facilitate enhanced performance and better partnership with the formal sector. In view of modernizing the city of Addis Ababa as the seat of the African Union, UN agencies and several international organizations, attempts are being made to clear informal operators that have congested residential areas, main streets pavements and the big business centers. To that effect special working spaces are being allocated around the peripheral areas of Addis Ababa and some open spaces within the city. Therefore, the tendency of the government to focus on policies and development strategies concerning informal sector development seems to be a positive step towards accepting the sector as a useful partner of development.

2.3 Conceptual framework of the study

The idea of informal sector has used in the context of society and development starting from the past up to the present and the sector played an important role in supporting livelihoods and contributing to the production and consumption activities in developing countries. However, no standard, internationally agreed definition has been arrived mainly because of its complex and heterogeneous nature, and different socio-economic background of different regions (Nuru, 2009). The idea was first used by Keith Hart, in his pioneering analysis of workers outside the formal labour market and who were predominantly self-employed in Ghana. He observed that

―price inflation, inadequate wage and an increasing surplus to the urban labour market have led to a high degree of informality in the income generating activities of the sub-proletariat. Consequently, income and expenditure pattern are more complex than is normally allowed for the economic analysis of countries. Government planning and the effective application of the theory in this sphere have been impeded by the unthinking transfer of western categories to the economic and social transfer of African cities‖ (ECA, 1993).

The informal sector was first appreciated by ILO (1972) as distinct socio economic force. The definition given by ILO is still widely accepted to represent the conceptual basis of informal sector activities and defined it as ―… all small-scale activities that are normally semi-organized and unregulated, use simple labour intensive technology … undertaken by traders, artisans and operators in work site such as open yards, market stalls, undeveloped plots, residential houses, and street pavements… not legally registered and in most cases not have license for carrying out business‖ (Ferej, 1996). It was only in 1993 during the fifteenth International Conference of Labour Statisticians (ICLS) that the informal sector acquired a proper definition based on production units, both in conceptual and statistical terms. Accordingly, informal sector is regarded as a group of household enterprises or unincorporated enterprises owned by household that include:  Informal own account enterprise, which may employ contributing family workers and employees on occasional basis,  Enterprise of informal employers which employee one or more employees in a continuous basis, and  Size of a unit below a specified level of employment and non-registration of the enterprise or its employees.

In Ethiopia, the Central Statistical Authority and the Ministry of Labour and Social Affaires (MoLSA) defined urban informal sector as ―home based or individual establishment activity operated by the owners with no or few employees…….., these establishment or activities include those engaged in market production which are not registered as companies or cooperatives which have no written book accounts and license, and have less than ten person engaged in the activity‖ (CSA, 2003).

The researcher has used the definition forwarded by CSA to differentiate informal activities from the formal one. Generally, the idea of informal sector covers a wide range of activities that combine two groups of different nature. Firstly, the informal sector is formed by the coping behavior of individuals or family in economic environment where earning opportunities are low. Secondly, the informal sector is a product of rational behavior of entrepreneurs that desire to escape state regulation. Finally the impact of the informal sector on urban poverty reduction represented by diagram. Dia-1 Conceptual Framework of the study

-Age

-Family Size Informal Poverty Sector -Level Education

-Access

Remittance

Source: Own competition

3. RESEARCH METHODOLOGY

3.1 Description of the Study Areas

According to the year 2014 statistical data, the population of the Southern Nations Nationalities Peoples region is estimated at 17837005. That accounts for approximately 20% of the total population of the country. Southern Nations Nationalities, Peoples Region State (SNNPRS) has 14 zones that consist of a total of 125 Woredas, including some autonomous Woredas. These Woredas consist of 3561 rural kebeles, 90 town centers and Hawasa is the administrative capital of the region.

Gamo gofa zone is one of the 14 zones in SNNPRS and there are five indigenous ethnic groups in Gamo Gofa Zone with distinct languages and cultural base. The zone has a total area of 12,581.4km2 and administratively consists 15 rural woredas namely, Arba Minch Zuria, Mirab- Abaya, Boreda, Chencha, Dita, Kucha, Daramlo, Bonke, Kemba, Zala,Ubadebretsehay, Oyida, Demba Gofa, Geze Gofa and Melakoza and two reform towns called Arba Minch and Sawla. The general elevation of the Gamo Gofa zone ranges from 680 to 4207 meters above sea level. The total population of the Gamo Gofa zone is estimated about 1901953. (CSA 2014)

Arba Minch town is the administrative and trading center of the zone, located at 505 km from Addis Ababa and 275 km south west of Hawassa and it got its name from the 40 springs flowing from the rock located in the nearby dense forest. According to the National Population and Housing Census carried out in 2014 by Central Statistical Authority (CSA), the population of the town was 135452. Out of this 68132 (50.3%) were males and 67320 (49.7%) were females. The town also comprises a financial, health, and educational facilities vital for the promotion of any investment activities in the town. The international airport established in the region, is set up at Arba Minch town.

The city gets grain products, livestock supply, natural resources (fuel wood and charcoal) labor from surrounding areas and some agricultural inputs and outputs from Hawasa and Shashemene cities. But, the city obtains manufacturing and commercial products and some construction materials from Addis Ababa while fish, fruits, and vegetables are sent from the town to other areas.

Location Map of the Study Area

Figure 1: Map of Study Area

3.2 Data Type and Methods of Data collection

3.2.1 Sampling Size Determination and Techniques

To determine the appropriate sample size for this study, Cochrans sample size determination formula was adopted (Quartey and Blankson, 2008) because of the population is infinite population. The sample is determine by using the minimum sample size formulae which adjusted for the total population of the study area is given by

3.2.1.1 Sampling Size Determination

The sample size is determined using the formula developed by Cochray (Cochran 1977)

no = ------1

Where

no = sample size

e = level of precision = 0.05,

z = standard error associated with the chosen 95 percent degree of confidence (1.96), and p = sample proportion in a population household participation in informal sector (0.41) and

(1-P) = sample proportion in a population household with non-participating in informal sector (0.59%), based on ILO, cited in Chaudhuri & Mukhopadh-yay (2010:8). Based on the above formula, the sample size becomes:

n = = 237 o

This is not the final sample size and in order to calculate the final sample size, one must consider the total target population of the study area. Therefore, Cochran‘s formula should be used to

21 calculate the final sample size by considering the total target population. Correct formula should be used to calculate the final sample size. These calculations are as follows

n1=

where,

N= total number of the target population of the study area, no= required return sample size according to Cochran‘s formula= 237, and

n1= the final sample size.

According to 2016 Arba minch ketema administration report, the total household in Arba Minch town is 22820. Accordingly, the appropriate sample size for this study becomes; n1= = 235

Therefore, the total final sample size for this study is 235 households Based on the proportion of households, 139 and 96 non-participants and participant household in informal sectors respectively, will be interviewed.

3.2.1.2 Sampling Techniques

In this study, multi-stage sampling technique will be employ. This data is also being collects as part of the study on informal sector participant households and formal sector participant households in Arba Minch town. Based on this information, Two sub-city will be selected purposively because of there are averagely many populations are found there, in compere to other sub-city of Arba Minch town and the two sub-city are the averagely bigger sub-city in the town in population (Household) size. In the second stage, four kebeles will be selected from two

22 sub city purposively namely Doyisa, Chamo, Dilfana and Gurba because of this four kebeles locational middle of the town that is why we will select those kebeles and the proportion of household in this four kebeles averagely higher than the remaining. Finally, random sampling (a proportionate random sampling technique) is used to select 235 participant in formal sector households and informal sector participants of households (for each kebeles will be use proportional sample size).

Table: 3.1 Proportionate sample size of household by their kebeles

Kebele Total household Sample of household Participant Non-participant (41%) (59%) Doyisa 2057 55 23 32 Chamo 1957 52 21 31 Dilfana 1938 51 20 31 Gurba 2907 77 32 45 Total 8859 235 96 139

Source: Owen survey

3.3 Methods of Data Analysis

The study will use Both Descriptive and Econometric Methods of data analysis are employed to analysis of the data.

3.3.1 Descriptive Statistics

Descriptive statistics are important tools to present research results clearly and concisely. They help one to have a clear picture of the characteristics of sample units. Descriptive statistics such as mean, standard deviation, percentages, frequency and inferential statistics such as chi-square test (for categorical variables) and t-test (for continuous variables) are used to compare and contrast different categories of sample units with respect to the desired characters so as to describe and then draw some important conclusions.

3.3.2. Econometrics Model:

It is the set of methods used to generalize from sample to population by performing hypothesis- testing, determining relationship among estimates of variables and making predictions. Inference is the objective of statistics; especially decision making and prediction plays a very important role for planning.

3.3.2.1 Propensity Score Matching (PSM) Method

Rosenbaum and Rubin (1983) are the first to develop the PSM statistical tool. The technique has attracted attention of social program evaluators (Dehejia and Wahba, 1999; Jalan and Ravallion, 2003). PSM is a non-parametric estimation method that works by reweighting the comparison sample to provide an estimate of the counterfactual of interest what the outcome of a Participant (beneficiary) household would have been had it not Participant in the sector. Since informal sector participants are poor households, the comparison of mean outcomes between Participant (beneficiaries) and non-Participant would lead to biased estimates. In order to circumvent this problem the study used the matching technique called propensity score matching method.

An important issue that needs to be overcome using this strategy is how to ensure that selection problem do not bias the result. The challenge here is to reconstruct a control group that has the same observable characteristics as the treated group. PSM pairs individuals in a treated group (households participating in informal sector) to individuals in an untreated group (households not participating in informal sector) using a set of observable information and assuming that the outcomes are independent of assignment to treatment, conditional on pre-treatment covariates, the method can lead to unbiased estimators of the treatment impact (Dehijia and Wahba, 2002).

3.3.2.2 Mathematical Specifications of PSM Method

In this study households who participated in the informal sector are considered as the treated group and households who did not participated in the informal sector are considered as control group. These, groups are a comparison group used to evaluate the effect of informal sector on treated groups‘ Poverty (livelihood). Ideally, the aim is to compare the level of economic and social well being of informal sector participant to that of non-Participant. This ensures that the

24 average treatment effect or effect of participation in informal sector on Poverty (livelihood) of households could be accurately estimated. Let and be the gross income and consumption expenditure for participants and non-participants respectively. The difference in outcome between treated and control groups can be seen from the following mathematical equation:

(1)

Where is an outcome of treated household, when he/she participating in informal sector, outcome of untreated household, when he/she not participating in informal sector and change in outcome as a result of treatment.

Let the above equation be expressed in causal effect notational form, by assigning as a treatment variable taking the value 1 if individual Participating in the sector and 0 otherwise. Mathematically can be written as:

(2)

Where is Participant effect of individual or change in outcome as a result of

Participating in the Business in which and are the potential outcomes participant and not participant household respectively and is whether household has Participat or not (i.e., whether a household participated in the informal sector or not).

However, one should notice that and cannot be observed for the same household at the same time. Depending on the position of the household in the sector, either or is unobserved outcome (called counterfactual outcome). Due to this fact, estimating individual participant effect is not possible and one has to shift to estimate the average participant effects of the population than the individual one. That means (population) Average Treatment Effect (APE) which is simply the difference of the expected outcomes after participation and non-participation. Mathematically can be expressed as:

( ) ( ) (3)

This measure answers the question what would be the effect if households in the population are randomly assigned to treatment. This makes it impossible to directly calculate by using cross- sectional data, the difference between the outcomes before and after treatment for each individual or household. Furthermore, Heckman et al. (1997), note, that this estimate might not be of importance to policy makers because it includes the effect for which the intervention are never intended.

Therefore, the equation (3) is modified to estimate the average treatment effects on the treated

(ATT); which is the most important evaluation parameter concentrates solely on the effects of informal sector. In the sense that this parameter focuses directly on those households who participated, it determines the realized effect of the sector and helping to decide whether the sector is successful or not. It can be expressed formally as:

( ) (4)

The term in the equation (4) is an unobserved counterfactual outcome of participated individuals may be subject to selection biases. If holds true, then non-participants can be conveniently used as the comparison group. The current study uses the PSM to handle the bias it solve the problem of multi-dimensionality which arises from the application of covariates matching procedure due to large number of covariates. According to Rosenbaum and Rubin (1983), the effectiveness of matching estimators as a feasible estimator for effect (impact) evaluation depends on the two fundamental assumptions those are conditional independence assumption and assumption of common support.

In matching the fundamental assumption, CIA, states that treatment assignment ( ) conditional on the attribute and the coverlet (X) is independent of the post-treatment outcome . In formal notation, this assumption corresponds to:

(5)

Where indicates independence, is a set of observable characteristics, outcome indicator of participant and non-participant. Hence, after adjusting for observable differences, the mean of the potential outcome is the same for and and . This implies that if one can control for observable differences in characteristic between the participating and non- participating group, the outcome that would result in the absence of treatment is the same in both cases. This is to say, we re-create the counterfactual outcome of non-treatment on the treated.

Following Rosenbaum and Rubin (1983), if is the propensity score (PS), then the balancing on pre-treatment given the PS is

(6)

The conditional average effect of treatment on the treated has a problem, if the number of the set of conditioning variables (X‘s) is high, and thus the degree of complexity for finding identical households both from treated and control groups becomes difficult. To reduce the dimensionality problem in computing the conditional expectation, Rosenbaum and Rubin (1983) showed that instead of matching on the base of X‘s one can equivalently match treated and comparison units on the basis of the ―PS‖ defined as the conditional probability of participating (treated) in informal sector given the values of X, notational expressed as:

(7)

The propensity score is defined as the probability of participation for household given a set which is households‘ characteristics . PS is derived from discrete choice model, and then used to construct the comparison groups. Matching the probability of participation, given covariates solves the problem of selection bias using PSM (Liebenehm et al., 2009). The distribution of observables is the same for both participants and non-participants given that the PS is balancing score (Liebenehm et al., 2009). If outcomes without the intervention are independent of participation given , then they are also independent of

27 participation given . This reduces a multidimensional matching problem to a single dimensional problem. Due to this, differences between the two groups are reduced to only the attribute of treatment assignment, and unbiased impact estimate can be produced (Rosenbaum and Rubin, 1983).

The second assumption is common support, the region where the balancing score has positive density for both participated (treated) and comparison units. This assumption rules out perfect predictability of D given that is:

(8)

The assumption that P(x) lies between 0 and 1: this restriction implies that the test of the balancing propensity is performed only on the observations who‘s PS belongs to the common support region of the PS of participated (treated) and control groups (Becker and Lchino, 2002). Individuals that fall outside the common support region would be excluded in the treatment effect estimation. This is an important condition to guarantee improving the quality of the matching used to estimate the ATT. Moreover, implementing the common support condition ensures that person with the same X‗s values have a positive probability of being both participant and non-participants (Heckman et al., 1999). This implies that a match may not be found for every individual sample. Rosenbaum and Rubin (1983) describe assumption one and two together as strong ignore ability.

Assuming that the CIA and the common support condition hold, the PSM estimator for ATT can be generalized as:

( | ) ( | ) ( | ) (9)

Where is the propensity score computed on the covariates X and Equation (9) is explained as: the PSM estimator is the mean difference in outcomes over the common support, appropriately weighted by the PS distribution of participants.

3.3.2.3 Implementation of the Propensity Score Matching

According to Caliendo and Kopeinig (2008), there are steps in implementing PSM. These are estimation of the PS using binary model, choosing a matching algorithm, checking on common support condition and testing the matching quality.

Step one: Estimation of the Propensity Scores (PS): First the PS was obtained using either logit or probit models to predict the probability of participation of household. According to Gujarati (1999), both provide similar results. Thus, for comparative computational simplicity logit model was used and run for the sampled households to estimate PS using households‘ pre- intervention characteristics (Rosenbaum and Robin, 1983) and matching is then performed using PS of each observable characteristics, which must be unaffected by the intervention. These characteristics include covariates variables that influence the participation decisions and the outcome of interest. The coefficients are used to calculate a PS, and participants matched with non-participants based on having similar PS. In estimating the logit model, the dependent variable is participation on informal business which takes the value of 1 if a household participated in the informal business and 0 otherwise.

Step two: Choice of the matching algorism: Estimation of the PS is not enough to estimate the ATT of interest. This is due to the fact that PS is a continuous variable and the probability of observing two units with exactly the same PS is, in principle, zero. Various matching algorithms have been proposed in the literature to overcome this problem. The methods differ from each other with respect to the way they select the control units that are matched to the treated, and with respect to the weights they attribute to the selected controls when estimating the counterfactual outcome of the treated. However, they all provide consistent estimates of the ATT under the CIA and the overlap condition (Caliendo and Kopeinig, 2008). Below, only the most commonly applied matching estimators are listed. Nearest Neighbour Matching (NNM), Caliper Matching, Kernel Matching, Radius and Caliper Matching

However, the most important question in using PSM is on how and which method to select these estimators. Clearly, there is no single answer to this question. The choice of a given matching estimator depends on the nature of the available data set (Bryson et al., 2002). In other words, it should be clear that there is no `winner' for all situations and that the choice of a matching estimator crucially depends on the situation at hand. The choice of a specific method depends on the data in question, and in particular on the degree of overlap between the treated and comparison groups in terms of the PS. When there is substantial overlap in the distribution of the PS between the comparison and treatment groups, most of the matching algorithms yield similar results (Dehejia and Wahba, 2002). A good matching estimator is that provides low pseudo-R2 value (Sianesi, 2004) and statistically insignificant likelihood ratio test of all regressors after matching that means a matching estimator which balances all explanatory variables between both groups, (Smith and Todd, 2005) and also expected to retain relatively larger observations for evaluating the impact of a participation.

Step three: Checking overlap and common support: Imposing a common support condition ensures that any combination of characteristics observed in the treatment group can also be observed among the control group (Bryson et al., 2002). The common support region is the area which contains the minimum and maximum PS of treatment and control group households, respectively. Comparing the incomparable must be avoided, i.e. only the subset of the comparison group that is comparable to the treatment group should be used in the analysis. Hence, an important step is to check the overlap and the region of common support between treatment and comparison group. One means to determine the region of common support more precisely is by comparing the minima and maxima of the PS in both groups. The basic criterion of this approach is to delete all observations whose PS is smaller than the minimum and larger than the maximum in the opposite group. Observations which lie outside this region are discarded from analysis (Caliendo and Kopeinig, 2008). No matches can be made to estimate the ATT parameter when there is no overlap between the treatment and non-treatment groups.

Step four: Testing the matching quality: Since we do not condition on all covariates but on the PS, it has to be checked if the matching procedure is able to balance the distribution of the relevant variables in both the control and treated group. The main purpose of the PSM is not to perfectly predict selection into treatment but to balance all covariates which mean it serves as a balancing method for covariates between the two groups. When differences in covariates are expected before matching, these should be avoided after matching. Consequently, the idea behind balancing tests is to check whether the PS is adequately balanced. In other words, a balancing test seeks to examine if at each value of the PS, a given characteristic has the same distribution for the treated and comparison groups. The basic idea of all approaches is to compare the situation before and after matching and check if there remain any differences after conditioning on the PS (Caliendo and Kopeinig, 2008). Rosenbaum and Rubin (1983), Dehejia and Wahba (2002), emphasized that the crucial issue is to ensure whether the balancing condition is satisfied or not because it reduces the influence of confounding variables. There are different approaches in applying the method of covariate balancing between treated and non-treated individuals. Among different procedures the most commonly applied ones are described below.

Standard bias (SB): One suitable indicator to assess the distance in marginal distributions of the X variables is the (SB) suggested by Rosenbaum and Rubin (1985). For each covariate X it is defined as the difference of sample means in the treated and matched control subsamples as a percentage of the square root of the average of sample variances in both groups. It is used to quantify the bias between treated and control groups. For each variable and PS, the SB is computed before and after matching as:

̅ ̅ (10)

√

Where ̅ and ̅ are the sample means for the control and treatment groups and and are the corresponding variance (Caliendo and Kopeining, 2008).

For binary data, the standardized bias is computed as:

(11) √[ ]

Where and proportion of the covariate in the control group, and proportion of the covariate in the sector group respectively

The Bias Reduction (BR) can be computed as:

) (12)

̅ ̅ Where standardized bias after match, and

√

̅ ̅ Standardized bias before matching

√

One possible problem with the SB approach is that one does not have a clear indication for the success of the matching procedure. t-test: A two-sample t-test to check if there are significant differences in covariate means for both groups (Rosenbaum and Rubin, 1985). Before matching differences are expected, but after matching the covariates should be balanced in both groups and hence no significant differences should be found. The t-test might be preferred if the evaluator is concerned with the statistical significance of the results. The shortcoming here is that the bias reduction before and after matching is not clearly visible.

Joint significance and Pseudo-R square: Sianesi (2004) suggests re-estimating the PS on the matched sample, i.e. only on participants and matched nonparticipants, and comparing the pseudo-R2 before and after matching. The pseudo-R2 indicates how well the regressors X explain the participation probability. After matching there should be no systematic differences in the distribution of covariates between both groups and therefore the pseudo-R2 should be fairly low. Furthermore, one can also perform a likelihood ratio test on the joint significance of all covariates in the logit model. The test should not be rejected before, and should be rejected after, matching. In our case, in order to test the matching quality of matching estimators the combinations of the above procedures are applied.

Estimation of standard error: Standard errors in psmatch2 are invalid, since they do not take into account the estimation uncertainty involved in the logit regressions (pscore). This makes testing the statistical significance of treatment effects and computing their standard errors is not a straightforward thing to do. The problem that the estimated variance of the treatment effect should also include the variance due to the estimation of the propensity score, the imputation of the common support, and possibly also the order in which treated individuals are matched. These estimation steps add variation beyond the normal sampling variation (Heckman et al., 1998). One way to deal with this problem is to use bootstrapping as suggested by Lechner (2002).

Bootstrapping: This method is a popular way to estimate standard errors in case analytical estimates are biased or unavailable. Recently it has been widely applied in most of economic literatures in impact estimation procedures. Each bootstrap draw includes the re-estimation of the results, including the first steps of the estimation (PS, common support). Bootstrap standard errors attempted to incorporate all sources of error that could influence the estimates.

Abadie and Imbens (2006), argue that using the bootstrap after NNM, until recently a common approach to estimating standard errors in evaluation studies, does not yield valid estimates. In other words, bootstrapping estimate of standard errors is invalid for NNM selection. Thus, calculating analytical standard error is applicable here. Bootstrapping standard errors for kernel matching estimators is not subject to this criticism because the number of observations used in the match increases with the sample size. Repeating the bootstrapping N times leads to N bootstrap samples and in our case N estimated average treatment effects. The distribution of these means approximate the sampling distribution and thus the standard error of the population mean. Clearly, one practical problem arises because bootstrapping is very time-consuming, computationally expensive and might therefore not be feasible in some cases (Caliendo and Kopeinig, 2008).

Eventually, using predicted probability of participation in the program (i.e. PS) match pairs are constructed using alternative methods of matching estimators. Then the impact estimation is the

33 difference between simple mean of outcome variable of interest for participant and non- participant households.

The difference involvement in informal sector between treatment and matched control households is then computed. The ATT is obtained by averaging these differences in informal sector outcomes ( ) across the matched pairs of households as follows:

∑ [ ] (13)

A positive (negative) value of ATT suggests that households who have participated in informal sector have higher (lower) of outcome variable non-participants.

3.3.2.4 Sensitivity Analysis

Recently checking the sensitivity of the estimated results becomes an increasingly important topic in the applied evaluation literature (Caliendo and Kopeining, 2008). Matching method is based on the CIA, which states that evaluator, should observe all variables simultaneously influencing the participation decision and outcome variables. This assumption is intrinsically non-testable because the data are uninformative about the distribution of the untreated outcome for treated units and vice versa (Becker and Caliendo, 2007). As outlined in equation (9) that the estimation of ATT with matching estimators are based on the un-confoundedness or selection on observables assumption. However, if there are unobserved variables which affect assignment into treatment and the outcome variable simultaneously, a ‗hidden bias‘ might arise (Rosenbaum, 2002). In other word, if treatment and outcomes are also influenced by unobservable characteristics, then CIA fails and the estimation of ATTs are biased. The size of the bias depends on the strength of the correlation between the unobservable factors, on the one hand, and treatment and outcomes, on the other.

It should be clear that matching estimators are not robust against this ‗hidden biases. Different researchers become increasingly aware that it is important to test the robustness of results to departures from the identifying assumption. Since it is not possible to estimate the magnitude of

34 selection bias with non-experimental data, the problem can be addressed by sensitivity analysis using Rosenbaum bounding approach proposed by Rosenbaum (2002) in order to check the sensitivity of the estimated ATT with respect to deviation from the CIA. The basic question to be answered here is whether inference about treatment effects may be altered by unobserved factors. In other words, one wants to determine how strongly an unmeasured variable must influence the selection process in order to undermine the implications of matching analysis.

The bounding approach does not test the un-confoundedness assumption itself, because this would amount to test that there are no (unobserved) variables that influence the selection into treatment. Instead, Rosenbaum bounds provide evidence on the degree to which any significance results hinge on this un-testable assumption. If the results turn out to be sensitive, the evaluator might have to think about the validity of his/her identifying assumption and consider other estimation strategies.

As noted above, it is not possible to estimate the magnitude of selection bias using observational data, instead the sensitivity analysis using the bounding approach that involves calculating upper and lower bounds, using the Wilcox on signed rank test. This rank tests the null hypothesis of no-treatment effect for different hypothesized values of unobserved selection bias. The central assumption of the analysis is that treatment assignment is not un-confounded given the set of covariates i.e., that equation (5) no longer holds. In addition, it is assumed that the CIA holds given and an unobserved binary variable . In other words the probability of participation needs to be complemented by a vector containing all unobservable variables and their effects on the probability of participation captured by ⁄ (14)

Where is the effect of on the probability of participation in the program, assuming that follow logistic distribution, the odds ratio of two matched individuals (let say and ), who are identical in observable characteristics in participating in the sector written as:

[ ] = (15)

Equation (15) states that two units with the same differ in their odds of participating in the sector (business) by a factor that involves the parameter and the difference in their unobserved covariates . As long as there is no difference in between the two individuals or if the unobserved covariates have no influence on the probability of participation ( = 0). This happens if the probability of participation will only be determined by the vector and the selection process is random. In contrast, if the unobserved covariates have influence on the probability of participation ( ); implied that two individuals with the same observed characteristics have different chances of participating in the informal sector (business) due unobserved selection bias.

In our case sensitivity analysis examined how strong the influence of and ( on the participation process needs in order to attenuate the effect (impact) of informal business on urban poverty.

Following Rosenbaum (2002), equation (15) can be rewritten as:

(16)

Both matched individuals have the same probability of participating only if =1 provided that they are identical in . Consequently there will be no selection bias on unobservable covariates. If =2, one of the matched individuals may be twice as likely to participate as the other agent (Rosenbaum, 2002). If is close to one and changes the inference about the treatment effect, the impact of participation on potential outcomes, the estimated effect is said to be sensitive to hidden bias. In contrast, insensitive treatment effects would be obtained if a large value does not alter the inference about treatment effects. In this sense, could be interpreted as a measure of the degree of departure from a study that is free of unobservable selection bias (Rosenbaum, 2002). Several values of bounds are calculated on the significance level, and hence, the null hypothesis of no effect of treatment on potential outcomes, is then tested.

3.4 Measurement of Poverty

A major challenge facing sub-Saharan Africa is to eradicate poverty. The definition of poverty remains the center of debate among policy analysts. This controversy has brought about gradual evolutions from the traditional definition as lack of income. In the 1980s and 1990s, the definition evolved from the notion of ―minimum level of subsistence‖ to the notion of ―relative deprivation‖ (Box et al., 2006). Other elements such as capabilities, dignity, autonomy, and empowerment are also included in the definition of poverty as it is a multi-dimensional.

But, a classic definition of poverty is the inability to attain minimal standards of living measured in terms of basic consumption needs or the income required for satisfying them. Poverty is thus characterized by the failure of individual, household or entire communities to command sufficient resources to satisfy their basic necessities (Adrian Gauci, 2007). The United Nations (UN 1995) defined absolute poverty as ―a condition characterized by severe deprivation of basic human needs, including food, safe drinking water, sanitation facilities, health, shelter, education and information. It depends not only on income but also on access to services‖. The poverty measure is decomposable in the sense that total poverty is a weighted average of the sub-group poverty levels (Foster,James., Greer J. and Eric Thorbecke, 1984).

Cost of basic needs approach(CBN) was used to determine poverty line, it takes into account both the food and non-food requirements, is the most widely used method of estimating poverty line because the indicators are more representative and the threshold is consistent with real expenditure across time, space and groups.

Steps to establish poverty line

3.4.1 Food poverty Line  Stipulate a consumption bundle that is deem to be adequate for the above mentioned level of minimum caloric requirement.  Pick a nutritional requirement for each food items which yield 2200 Calories per person per day.  Estimate a quantity of each bundle in gram that yield a calorie requirement of that food item per person per day.

 Then convert quantity in gram of each item consumed to kilogram. To obtain annual adult equivalent consumption of food items multiply food items in kg by 365 days.  Then these bundles giving the minimum caloric requirement is value at local market prices to get a food poverty line of the study area. Which become minimum amount of money that a household needs to purchase basic-needs of food bundle.

3.4.2 Non-Food poverty Line

After setting the food poverty line a specific allowance for the non-food goods is made i.e. with the spending of the poor is added to the food poverty line which will yield the overall poverty line. According to World Bank (2005), there are different approaches to take part of measuring non-food poverty line Practice varies widely from one analyst to the next. But most studies set the poverty line as a share of mean expenditure/income or identified the poor using some percentage (e.g. 20%, 25%) of the income or expenditure distribution. Therefore, in this study to account for the non-food expenditure, the food poverty line is divided by the food share of the poorest quartile or quintile (Ravallion, 2003) & (MoFED, 2013). Finally the analysis in this paper focus on poverty among households; if a household is deem to be poor, all its members are counted as poor. The implicit assumption here is that all individual members of a household benefit equally (or in a constant proportion, depending on their age and gender, called adult equivalence scale) from the household's expenditure .we have use absolute poverty lines for the analysis in this paper.

3.4.3 Measurement of Poverty

The magnitude and measure of Poverty in the study area is computed based on information available on a welfare measure, in this study case adult equivalent consumption level and a poverty line, for each household or individual. To construct a summary measure of the extent of poverty there are varies types of poverty indices but the most widely uses and employee in this study is the well-known FGT (1984) class of poverty measures.

Many alternative measure of poverty is exists. However, three main indicators are often used to measure poverty: poverty incidence, poverty gap index and severity of poverty.

3.4.3.1 The incidence of poverty (headcount index)

This measure the share of the population whose income or consumption is below the poverty line; that is, the share of the population that cannot afford to buy a basic basket of goods either food or non-food with the stated amount.

P0 =

Where,

P0 = poverty head count ratio

Np = Number of households below the given poverty line

N = Total number of households in the sample

3.4.3.2 Poverty gap index

This measures the depth of poverty, considering the number of poor people and how far the poor households are from the poverty line (how poor they are). This measure captures the mean aggregate income or consumption shortfall relative to the poverty line across the whole population. It is obtained by adding up all the shortfalls of the poor (assuming that the non-poor have a shortfall of zero) and dividing the total by the population. In other words, it estimates the total resources needed to bring all the poor to the level of the poverty line (divided by the number of individuals in the population). Using the index function, we have.

Gi =

P = ∑ ______1 1

P = ∑ ______2 1

3.4.3.3 Severity of poverty (which is squared poverty gap index)

This shows not only the distance separating the poor from the poverty line, but also the degree of inequality among the poor that is higher weights is placed on those households further away from the poverty line.

Formally:

2 P = ∑ ( ) 2

3.5 Definition of Variable and their Expected Hypothesis

Dependent variables

 Participation of Household on informal sector (PARTHIS)

It is a dummy variable that represents participation of the household in informal sector. For the household who participated in informal sector the variable takes the value of one whereas it takes the value of zero for the household who did not participated in informal sector (business).

3.5.1 Outcome variables

To calculate the ATT (Average Treatment effect on Treated) using PSM, the study uses a set of outcome variables, which the Informal Sector use as Poverty outcome indicators. The study considers household expenditure on consumption in our case food and non-food expenditure. The above interest of outcome indicators comparison for the sample household is broader and the gross that may cover the effect of informal sector on urban poverty to make a clear picture of the findings of the survey with the basic elements of conceptual framework of urban poverty.

Total consumption expenditure (TCE): This refers to the total household expenditure on food and non-food items for one month preceding the survey. It is a continuous outcome variable

40 measured in ETH Birr representing total household consumption per month. The food consumption includes food items that the household purchased or produced (used for own consumption). The non-food consumption is based on total expenditures on non-food items such as supplies, utilities, clothes and etc.

In order to investigate the effects on individuals using quantitative techniques, some assumptions may need to be made, for example, in this study it is assumed that household members cooperate in all activities. Based on the assumption, aggregate household livelihoods indicators are converted into adult equivalences (AE) unit to adjust for household size and composition. Here after any reported of the estimate of ATT (Average Treatment effect on Treated) of livelihood indicators are converted into (adult equivalences) AE, if any it would be specify. Therefore, in this study all outcome indicators expressed in terms of per adult equivalences (AE) per Month and easily monetized their food consumed from own produced and purchased items before one month the data collection periods.

3.5.2 Independent Variables

 Age of Household Head (AGE)

It is a continuous variable and refers to the age of the head of the household. It is assumed that households with an older head have control over more resources, are more experienced and more responsibility. Households with young household heads often consume more than save. However, it is difficult for them to reduce poverty because they are consuming more. Some empirical studies have demonstrated that middle age group is higher number in participant to operate in informal sector these groups are high risk taker and ready to fight hardship of bad working condition that of harassment from Government officials ,obstacles and constrains than aged grouped (Etsubdink Sibhat (2013)). Therefore, it is expected that this variable would have positive influence on participation in informal business with increasing of age of the household head.

 Gender of Household Head (GEND)

This is a dummy variable, which takes a value of 1 if the household is male and 0 otherwise. Gender differentials among urban households play a significant role in the economic performance of a given household. In this study, it is assumed that male household heads have more exposure and access to resource and information regarding importance of informal business participation than female household heads. This variable is expected to have positive effect on the participation of informal business.

 Educational Level of Household Head (EDULHH)

It is a continuous variable and defined as the number of schooling years received by the household head. Many Researchers argue that better-educated urban households are expected to depend more on self-finance and on formal sector (business), because they may be better able to exploit investment opportunities and to better understand loan regulations and the borrowing procedures of the formal sector. Hence, households with higher education levels would prefer to get credit from the formal sector instead of from informal sources. Entry barriers into informal sector are quite negligible for most of the operators, because in most cases the business requires no this much educated People. The empirical result shows that by Selamawit G/ Meskel (2008) number of vendors decrease when education level increases since they have better job opportunities in the formal sector. On the other hand those who are less educated have less opportunity in the formal sector and hence they participate in informal sector.

 Household Family Size (HFS)

This variable is a continuous variable and defined as the total number of people in one household who have share the same meal. It is assumed that household with larger family size participate more time on business. Greater household size represents a bigger demand for consumption. Therefore, in the present study this variable is expected to have Positive effect on participation of informal sector

 Household Head Saving Habit (SAV)

Saving helps people to plan for future expense, cope with stochastic crises and cover unanticipated expenses. This variable Measures how much percent of the income they save in birr. Generally it includes not only money/birr but also assets and other landed property. However for the purposes of this study, the amounts of money and asset people have in the formal financial institutions and informal financial institutions. This variable is dummy If the participant save from day to day activity or Monthly 1 and 0 elsewhere. Saving and poverty reduction have direct relationship so the people have high saving culture they have more opportunity to out from poverty.

 Distance from Market (DISMAR)

It is a continuous variable measured as the distance in walking time (in minute) that the household travel to reach their business center. It hypothesized to have a Positive and Negative contribution to the participation of informal sector. Closeness to market centers may motivate Households to provide market-oriented production because of easy access to inputs, transport facilities and price externalities. Therefore, closeness to market place is expected to be positively correlated with Household participation in informal sector.

 Household Head Migration status (MIS)

Migrants move to the town with high expectation of livelihood improvement. However, they are obviously confronted with a host of challenges and difficulties. This variable Measures the income distribution of the business to both Migrant and Natives People. If the household Native 1 that mean the household Head Boren in Arba Minch Town and 2 elsewhere. The expected out came has migrant Household income is less than the Native People.

 Household Head access to Remittance (HHARE)

It is the households‘ access to remittances which is assumed to affect the per-capita income of the household positively and reduces the incidence of household poverty. The likelihood of a household receiving remittance increase choice of diversification in participating in formal and

43 informal business activities. This Variable is Dummy variable, if household have any remittance Local as well as foreign remittance 1 and 0 elsewhere.

 Household Head Health Status (HHHES)

Health is the decisive factor for life, one with poor health conditions will have a poor living standard (welfare). Household labor is often one of the largest means of earning income the urban people can rely upon. Lack of proper health will make the family survival challenging because poor health make the family unproductive. Proper health is an essential thing for the well-being of individuals and without which life would be difficult. Some operators noted health problem as the main operational problem to run the activity (Amene Afework 2011). Therefore, 1 if the household had one or more seriously ill member in 12 months or one year prior to the administration of the survey and 0 otherwise. In this study it is hypothesized that if illness strikes, not only working days are lost, but also part of saving will be spent for medical treatment, as a result of which the probability of being trapped in poverty is likely to rise.

 Household Head Working Time per Days (HHWOTDA)

The informal sector activity requires longer working time per days because of the income are more depend on the individual working time per day. This variable is continues variable and the expected Hypothesis is households spent high working time per day it has positive relation with poverty reduction.

 Place of Operation

Working Area is very important to formal sector even if informal sector. This variable is dummy variable If it a households has place of operation 1and 0 elsewhere. The expected hypothesis is households which have placed to work are direct relationship with the alleviation of poverty.

Amene Afework in 2011 found that problem of housing and/or suitable working place is the main challenge for informal sector participant in Dejen Town. As a result, most of the informal sector participants were run their activity in rented and crowded houses which is incapable to absorb all the clients.

From those explanatory variables we can select the variables which significantly affect the effect of informal business in urban poverty in order to build appropriate model.

Before proceeding to estimate the data using logit model, different tests are undertaken. One of the tests is checking the existence of multi-colinrarity between explanatory variables. The presence of multi-collinearity among the variables seriously affects the parameter estimates of any regression model. The Variance Inflation Factor (VIF) technique are employed to detect the problem of multi-collinearity for the all explanatory variables (Gujarati, 2004). VIF can be defined as:

(17)

Where is the squared multiple correlation coefficient between and other explanatory variables. The larger the value of VIF, the more troublesome it is. As a rule of thumb if a VIF of a variable exceeds 10, the variable is said to be highly collinear. For dummy variables if the value of contingency coefficients is greater than 0.75 the variable is said to be collinear.

Finally, the effect of informal business participation on urban poverty are estimated using STATA 12.0 software using the PSM algorithm (psmatch2) developed by Leuven and Sianesi (2003).

Table 3.2: Description of the variables, measurement and their expected hypothesized

Variable Name Type Definition Measurement Dependent Variable PARTHIS Dummy Participation in informal 1 for Participant Household and sector 2 for non-Participant Household Outcome Variable Income Continues Income per AE Birr Food and non-Food Consumption Birr Consumption expenditure per AE Continues Independent Variable Age(AGE) Age of Household Head Number of Year Continues

Gender of Household Dummy Sex of Household Head 1 if Female and 2 Male Head(GEND) Education Level of Educational Level Schooling Years Household Head Continues (EDULHH) Household Family Size Continues Family Size Number (HFS) Household Head Saving Dummy Saving Habit 1 if have Saving Habit Habit (SAV) 2 if not have Saving Habit Distance from Market Distance from Market to Walk (DISMAR) HH Continues Household Head Dummy Migration Status of 1 if Native 2 Migrant Migration Status(MIS) HH Household Head access Access to Remittance to 1 if HH have access to to Remittance(HHARE) HH Remittance Dummy 2 if HH not have access to Remittance Household Head Health Dummy Health Status of HH 1 if ill Member have Status(HHHES) 2 if no ill Member Have Household Head Working time per day of Time Working Time per HH Continues day(HHWOTDA) Place of Operation Dummy Place of Operation 1 if have a place of operation 2 if not have a place of operation Sources: own computation

CHAPTER FOUR

4. Results and Discussion

4.1. Descriptive Results

The descriptive statistics of sample households were computed for both participants and non- participants of informal sector. The objective was to assess and describe the differences and similarities among participants and non-participants of informal Sector in terms of their demographic and socio-economic characteristics. Besides, it was run to observe the distribution of the independent variables. The socio-economic and institutional characteristics of the sampled households such as sex, age, education, family size, Saving Habit, Migration Status, Access to Remittance and Health Status, were hypothesized to affect participation in the program in turn the outcome variables such as Monthly consumption expenditure of Households and total income in general. Of the total sample respondents interviewed and questioned 96 were Participant‘ and the rest were non-Participant‘ of Informal sector in the Study Area.

4.2 Description of informal business sector in Arba Minch

There is no statistical data available in the town in relation to the figurative representation of informal sector operators, their living patterns and other aspects etc. Although this is the case, people are significantly engage in various self-created jobs in the town and over represented in such informal business. Different informal activities are existed in Arba Minch town. These includes street vendors, selling cooked foods/drinks, selling clothes/shoes, bicycle/motor repairing and renting, vegetable/fruit vending, beauty work, shoes polishing, brokers, Kolo selling, Prostitution, petty trading etc. However, the eight activities have been expanding than other informal activities.

Demographic analysis is concerned with the size, composition, and distribution of populations; their patterns of change over time through births, deaths, migration; and the determinates and consequences of such changes population studies yield knowledge important for planning, particularly by governments, non-government organization and environmental preservation Such studies also provide information needed to formulate government population policies, which seek to modify demographic trends in order to achieve economic and social objectives in the town as well as country level.

4.3 Demographic and socio-economic characteristics of sample households

Informal sector participants mainly poor Households; hence, assessment of its impact requires a clear picture of the households‘ demographic, socio-economic and location characteristics of the sample household. Both continuous and discrete variables were used in order to describe the sample households and a combination of different descriptive statistics was performed on the household data to inform the subsequent empirical data analysis. Table 4.3 and 4.4 summarizes the information pertaining to the households‘ observable characteristics of the respondents.

Table 4.3: Distribution of sample households for continuous variables Variable Participant Non-Participant Total t-test (N=96) (N=139) Mean SD Mean SD Mean SD Age 34.37 4.67 34.67 5.70 35.02 5.21 1.38 Education 6.01 4.03 11.23 2.31 8.62 4.19 -0.48* Family Size 4.20 1.58 3.15 1.93 3.38 1.77 -0.83* Working Time Per 10.63 2.73 10.43 1.47 11.83 2.50 -3.28 Day Sources: own computation

Significant differences are indicated with *p<0.1:**p<0.05:***p<0.01

As the survey result indicates that the mean of the two groups were significantly different with respect to educational level (years of schooling) and Family size at 1%, and 1% probability levels. In other word, on average, participant households have relatively less years of schooling, and averagely higher member of family. In contrast to participants, non-participants have relatively higher years of schooling, and relatively small member of family. Moreover, the survey result shows that there are no mean differences in term of age, and working time per day was found between the two groups. This implies that both participants‘ and non-participants are closing similar in those observable characteristics of the sample households. In terms of dummy variables distribution of the sample households‘, Table 4.4 summarized the respondents‘ individual demographic characteristics and revealed that there is no significant difference in the individual characteristics of the survey respondents. That is, both participants‘

48 and non-participants are close similar in all of the variables of demographic characteristics i.e., sex distribution, access to remittance, health status, place of work, migration status, and saving habit. In other word all dummy variables described in Table 4.3 show statistically insignificant differences (p>0.1) between participant and non-participant households. Table 4.4: Distribution of sample households for dummies variables Variable Participant Non-Participant Total -value No No % No % % Gender Male 56 58.33 71 51 127 54 0.20 Female 40 41.66 68 49 108 46 All total 96 100 139 100 235 100 Saving Habit If have Saving habit 51 53.13 105 75.59 156 66.38 If not have Saving habit 45 46.87 34 24.46 79 33.62 0.43 All total 96 100 139 100 235 100 Migration Status Natives 40 44 91 65.46 131 55.74 0.30 Migrant 56 56 48 34.54 104 44.26 All total 96 100 139 100 235 100 Access to Remittance Ifhave remittance 32 33.33 72 51.80 104 44.26 If not have remittance 64 66.66 67 48.20 131 55.74 All total 96 100 139 100 235 100 0.19 Health Status If ill member have 32 33.33 51 36.70 83 35.32 If not ill member have 64 66.66 88 63.30 152 64.68 0.12 All over 96 100 139 100 235 100 Place of Operation Have a place of 45 46.88 116 83.45 161 68.51 operation

Not have a place of 51 53.12 23 16.55 74 31.49 0.51 operation All over 96 100 139 100 235 100

Significant differences are indicated with *p<0.1:**p<0.05:***p<0.01

The summary of variables in Table 4.3 and 4.4 revealed that the t-test and chi-square test results of the sample households are quite similar in terms of several key demographic characteristics: gender, marital status, saving habit, migration status, access to remittance, health status, and place of operation are statistically insignificant that means their mean distribution not different from zero. However, the descriptive results show that there were statistically significant differences observed between the group households before intervention. More particularly, the main differences between the two groups of households were observed with respect to educational level and family size.

4.4 Measurement of Poverty

Poverty line can be referred to as the level of welfare which distinguishes poor households from non-poor households. It is a pre-determined and well-defined measure of income or value of consumption (expenditure). Poverty lines are often drawn either in relative or in absolute terms. In the former, a proportion of the mean expenditure is taken as the poverty line, usually the one- third (which defines the core poverty line) and two-third (which defines the moderate poverty line) of mean expenditure have been commonly used. Relative to consumption, income is generally easier to report and is available for much larger samples, providing greater power to test hypotheses. Accordingly, this study utilized the mean per adult equivalent household income (MPAEHI) as a measure of relative poverty line. The mean per adult equivalent household income of the sample respondents was determined by first dividing the total annual income of each household for all households adjusted for adult equivalent. Dividing mean per adult equivalent household income by 12 months would result to mean monthly per adult equivalent household income. Two-third (2/3) of the MPAEHI mean monthly per adult equivalent household income is poverty line. Hence, extremely (core) poor, moderately poor and non-poor household were identified based on poverty line. Those households whose income is less than one-third (1/3) of MPAEHI were classified as extremely poor, less than two-third (2/3) of the

MPAEHI as moderately poor, while non- poor are those whose mean monthly per adult equivalent household income is more than poverty line. In measure of extent of poverty, the choice of income or consumption expenditure as best indicator for living standard measurement of households is another point of debate. Government of Ethiopia and most analysts prefer to use current consumption as an indicator of living standard measurement because income of the people often varies over time. In this study, to address dimension of poverty in the study area, the FGT poverty measure that was introduced by (Foster, Greer, and Thorbecke, 1984) was used. The first step was by distinguishing between the poor and non-poor using poverty line. Poverty line is monthly per capita consumption expenditure per person or a cut of living standard level below which an individual is considered to be poor (Rangarajan, 2014, MoFED, 2013; Doyle, 2003; Ravallion, 1992) cited by Dr Melkamu Mada.

Based on data from households, this study used three poverty dimension instruments that were identified by (Foster, Greer, and Thorbecke, 1984) to achieve the objective related to the extent of poverty in Arba Mich town informal sector participant and non-participant. These included headcount index; the poverty gap index; and severity index of poverty. Using these three poverty dimension instruments we identified the percentage of the poor (headcount index), the aggregate poverty gap (poverty gap index), and the distribution of income among the poor (poverty severity index).

4.4.1 Extent of poverty in Study Area

This analysis explains how we construct summary measures for the socio-economic characteristics of poverty in the study area. Extent of poverty can be easily summarized using poverty head count index (P0), poverty gap index (P1) and Poverty severity index (squared poverty gap) (P2). These indexes were computed and the results presented on Table 4.5.

Table 4.5: Extent of Poverty in Arba Minch Town informal sector Participant

Study Area Head count Index Poverty gap Index Poverty Severity (Poverty Incidence) (Poverty Depth) Index Arba Minch Town 0.58 0.32 0.17 Source: Own Survey

Table 4.5 shows situations of absolute poverty as measured by different poverty scales. With a poverty line of 314 per person per month for Arba Minch town informal sector participant and non-participant.

4.4.1.1 Incidence of Poverty or Headcount Index (P 0)

Table 4.5 shows different poverty indexes as measured by different poverty measuring approaches in Arba Minch town informal sector. Our calculations have used own computed (settled) poverty line. Using a poverty line of ETB.314 for Arba Minch town urban informal sector participant and non-participant the data result in the table shows the poverty headcount (incidence of poverty) was 0.58 in Arba Mich town. It shows share of the population that is poor, the proportion of the population for which consumption expenditure is less than Rs.314.Out of Two Hundred Thirty Five Households sampled households in the study area, one hundred twenty seven households were categorized as poor. This means 54 percent of sampled households are under poverty line in Arba Minch town. For one hundred twenty seven household consumption expenditure is less than ETB.314 per person per month. According to Participant and non- participant seventy and fifty seven household respectively under poverty line this means under poverty line fifty five present are participant of informal sector in Arba Minch town and forty five present are non-participant of informal sector. This is almost equal to the national poverty line, National poverty line states that 315.08 Ethiopian Birr (Melkamu Mada and Richard Kwasi Bannor 2015)

4.4.1.2 Depth of poverty or Poverty Gap Index (P1)

Poverty gap index depicted in table 4.5 indicates the extent to which the per-capita expenditure of the poor falls below the poverty line in Arba Minch town. The poverty gap index was 0.32 for

52 the study area. Poverty gap index result shows poverty is deeper among sampled households in Arba Minch town. Using poverty gap information to assess how many resources would be needed to eradicate poverty through cash transfers perfectly targeted to the poor is important. In Arba Minch town on average 32 percent of the poverty line cash transfer needed to lift each poor person out of poverty.

4.4.1.3 Poverty severity or squared poverty gap (P2)

The squared poverty gap index is not easy to interpret as compared to headcount index and Poverty gap index however, it has the advantage of reflecting the degree of inequality among the poor, in the sense that the greater the inequality of distribution among the poor and thus the severity of poverty, the higher is the squared poverty gap index.

Even though households, whose consumption expenditure lies below poverty line have common name ―poor‖ the degree of poverty varies from one to another. Therefore, poverty severity index measures variation in the poverty level of individual households. The result indicates in table 4.5 shows that 17 percent variation among poor households in the study area. This indicates the degree of inequality among the poor in Arba Minch town informal sector Participant and non- participant was higher. There is clear monthly per person expenditure difference among poor in the study area.

4.5 Impact of informal sector on the poverty of participants

4.5.1 Consumption expenditure of participant

Consumption expenditure is a useful indicator and measure of household welfare. It provides a better reflection of differences in permanent income. The measure of consumption used in this paper is sum total of food and non-food consumptions. Table 4.6: Differential impact on consumption expenditure

Consumption Participant Non-Participant Total expenditure on Mean SD Mean SD Mean SD t- test Food 322.35 126.22 311.54 94.37 351.95 118.32 -5.25*** Non-food 221.39 85.45 214.22 67.69 287.81 83.94 - 6.31*** Total 543.74 211.67 525.76 145.67 639.76 192.64 - 6.02*** Source: own computation, Significant differences are indicated with *** p<0.01

When comparing total household consumption expenditure, food and non-food expenditure for different items for treated and control households in Table 4.6, it is clear that participation in the informal sector is associated with differences in households‘ consumption expenditure. A simple mean difference comparison shows that program and non-program households had on average spent Birr 322.35 and 221.39 and Birr 311.54 and 214.22 per month on food and non-food expenditure and the mean difference between the groups were significantly different at 1% probability levels respectively. This implies that households in the sector are benefits from tax and non-participant is pay tax this is the main difference between the groups.

4.6 Empirical Results of Econometric Estimation

The descriptive analysis presented in the previous section shows that there are substantial differences in the underlying characteristics of treated versus control households as well as in their incomes and consumption expenditure. However, based on a simple comparison of means it is impossible to identify causality and to attribute the observed differences in Poverty (livelihood) outcomes to the impact of the informal scoter. This section presents an econometric

54 analysis such as Logit model and then different PSM steps to estimate the causal impact of participation in the informal sector on household Poverty (livelihood). According to Caliendo and Kopeinig (2008), there are steps in implementing PSM. These are estimation of the PS, choosing a matching algorism, checking on common support condition, testing the matching quality and test sensitivity analysis.

The first step in PSM method is to estimate the PS. When estimating the PS in the binary treatment case, logit and probit models usually yield similar results. Hence, the choice is not too critical, even though the logit distribution has more density mass in the bounds (Caliendo and Kopeing, 2008). Therefore, logit model was applied to predict PS for the PSM method in this study. 4.6.1 Choice of matching algorithm

Prior to non-parametrically estimate the impact of informal sector, it needs to well specify the PS for treatment. Estimated PS results for the improvement on poverty (livelihood) are aimed at checking whether our cross-sectional matching estimators are sensitive to the choice of a particular sub-sample, the common support condition is imposed and balancing propensity is set and satisfied in all regressions at 1% significance probability level.

Logit model was used to predict the probability to participate in the informal sector and included different ranges of household characteristics as regressors. Results of four different matching algorithms are reported in Table (see in Appendix) and are useful in order to check the consistency of the estimated causal effect, which may be affected by the set of exogenous variables used to estimate the PS (Smith and Todd, 2003).

Alternative matching estimators were tried in matching the treatment and control households in the common support region. The final choice of a matching estimator was guided by different criteria such as equal means test referred to as the balancing test (Dehejia and Wahba, 2002), pseudo-R2 and matched sample size. Specifically, a matching estimator which balances all explanatory variables (i.e., results in insignificant mean differences between the two groups), bears a low pseudo-R2 value and results in large matched sample size is preferable in that it makes less likely that the unobservable remain out of the matching process.

The estimated results of the choice of a matching estimator‘ tests based on the above mentioned performance criteria. After looking into the results, it has been found that kernel matching with a band width of 0.10 is the best estimator for the data at hand. As such, in what follows estimation results and discussion are the direct outcomes of the kernel matching algorithm based on a band width of 0.10. Kernel matching associates the outcome of the treated household with the matched outcome that is given by a kernel-weighted average of all control groups for participant of informal sector. Since the weighted averages of all participants in the control group are used to construct the counterfactual outcome, kernel matching has an advantage of lower variance because more information is used (Heckman et al., 1998).

4.6.2 Imposing common support condition

As stated before, four main tasks should be accomplished before one launches the matching task itself. First, predicted values of program participation (PS) should be estimated for all households in the program and outside the program. Second, a common support condition should be imposed on the PS distributions of household with and without the participation and discard observations who‘s predicted PS fall outside the range of the common support region. Third, best matching algorism should be identified to checking whether our cross-sectional matching estimators are sensitive to the choice of a particular sub-sample or in order to check the consistency of the estimated causal effect, which may be affected by the set of exogenous variables used to estimate the PS. Fourth, matching quality should be test to check the balancing of PS and covariate or to balance the distributions of relevant variables in both groups. Fifth, test overlap and conditional independence assumptions to test plausibility of assumption. Sixth, estimating ATT, and finally sensitivity analysis should be done in order to check the robustness of the estimation (whether the hidden bias affects the estimated ATT or not).

The common support region would be lie between 0.084 and 0 .987(mean=0.665) for participant households and between 0.006 and 0.960(mean=0.334) for control households. The common support region would then lie between 0.084 and 0.960. In other words, households whose estimated PS are less than 0.084 and larger than 0.960 are not considered for the

56 matching exercise. As a result of this restriction, 59 households (20 treated and 39 control households) were discarded from the analysis.

Table 4.7: Distribution of estimated propensity scores

Group Observation Mean STD Min Max Total households 235 0.50 0.28 0.006 0.987 Treatment households 96 0.67 0.24 0.084 0.987 Control households 139 0.33 0.23 0.006 0.960 Source: Own survey result

4. 6.3 Testing overlap and conditional independence assumptions

The result indicated that the value of pseudo R2 is fairly low after matching indicating that the unconfoundedness assumption is plausible. In addition to this, the study uses PS to test the plausibility of the overlap assumption. The results of matching exercise indicated that there appeared unmatched observations in the treated groups before common support condition is imposed. However, after matching the data using kernel matching method with band width 0.1, the common support condition has trimmed out a total of 59 participant observations from the model signifying that the overlap assumption is also plausible for the estimator.

All of the above tests suggest that the matching algorithm we have chosen is relatively best with the data we have at hand. Thus, the process of matching thus creates a high degree of covariate balance between the treatment and control samples that are ready to use in the estimation procedure. Therefore, we can proceed to estimate ATT for households. 4.6.4 Testing the matching quality

After choosing the best performing matching algorithm the next task is to check the balancing of PS and covariate using different procedures by applying the selected matching algorithm(in our case kernel matching). As indicated earlier, the main purpose of the PS estimation is not to obtain a precise prediction of selection in to treatment, but rather to balance the distributions of relevant variables in both groups. The balancing powers of the estimations are ascertained by considering different test methods such as the reduction in the mean standardized bias between

57 the matched and unmatched households, equality of means using t-test and chi-square test for joint significance for the variables used. The mean standardized bias before and after matching are shown in the third columns of Table see in Appendix, while column six reports the total bias reduction obtained by the matching procedure. In the present matching models, the standardized difference in X before matching is in the range of 2.68% and 77.58% in absolute value. After matching, the remaining standardized difference of X for all covariates lie between 0.1% and 9.30%, which is below the critical level of 20% suggested by Rosenbaum and Rubin (1985) and also below 10% suggested by Shadish et al. (2008) and Steiner et al. (2010) this implies that the matching procedure close enough to establish balance. In all cases, it is evident that sample differences in the unmatched data significantly exceed those in the samples of matched cases. The process of matching thus creates a high degree of covariate balance between the treatment and control samples that are ready to use in the estimation procedure.

Similarly, The t-values before matching one third of chosen variables exhibited statistically significant differences while after matching all of the covariates are balanced. Table 4.8: Chi-square test for the joint significance of variables

Ps R2 LR chi2 p>chi2 MeanBias MenBias B R %Var Unmatched 0.275 80.19 0.000 32.9 21.3 143.4* 1.05 14 Matched 0.009 2.41 1.000 5.1 5.5 21.8 1.14 14 Source: own estimation result * If B>25%, R outside [0.5; 2]

The low pseudo-R2 and the insignificant likelihood ratio tests support the hypothesis that both groups have the same distribution in covariates X after matching (see Table 4.8). These results clearly show that the matching procedure is able to balance the characteristics in the treated and the matched comparison groups. Therefore, these tests results used to evaluate the effect of informal sector between the groups of households having similar observed characteristics. This allowed comparing observed outcomes for participants with those of a comparison groups sharing a common support.

4.6.5. Treatment effect on the treated (ATT)

In order to attain the second objectives, the following impact indicators of the treatment effect have been performed using the already mentioned PSM model.

The sections above discussed the methods and various procedures adopted to control the sample of selection biases. Once tests showed that both group (treatment and control) were at par, the average treatment-on-treated effect (ATT) and the t-statistics (within the bootstrapped standard error is obtained after 100 replications). As discussed in detail below across each dimension, statistically significant values provide strong evidence that disparities in both groups did not occur merely by chance, but attributable to program participation.

4.6.5.1 Effect on informal business sector on consumption expenditure

Food consumption is most important in the lives of the poor and it serves as one of and appropriate indicator of their livelihoods. Moreover, consumption is important to maintain and increase productivity of human capital by ensuring good education and health status. For analyzing the participant impact on livelihood improvements the study used household food expenditures. The ATT estimates measures the impact of participation on the consumption expenditure, food expenditure and non-food expenditure summarized here below on in Table 4. 9.

Table 4.9: Average treatment effect on the treated (ATT)

Variables Treated(Participant Control Difference SE 1 t-value of informal sector) (Non - Participant) Food consumption 339.20 316.68 42.52 29.71 2.42** Non-food 299.13 281.43 57.70 26.23 2.20** consumption Total 638.33 598.11 100.22 46.84 2.14** Source: model estimation The bootstrapped SE is obtained after 100 replications ** Significant at 15% probability level

Table 4.9 shows that the ATT estimates are positive and statistically significant for consumption expenditure. This indicates that after accounting for selection bias the participants‘ household are more secured as compared to the non-participant‘. This implies that informal sector participation on the other hand does lead to an increase in its average consumption expenditure compared to that of non-participants. A likely reason for this might be due to absence of trade union contribution, absence of official protection and recognition for their employ, Non coverage by minimum wage legislation and social security system, predominance of own-account and self- employment work, absence from tax and other government rule and regulation.

The mean food expenditure of the treatment and control group for one month preceding the survey were around Br. 338.20 and Br. 316.68, respectively. The result demonstrates that the two groups are significantly different in terms of mean food expenditure. Mean that the informal sector had statistically significant positive impact in terms of food expenditure by the participant households and informal sector enabled the participant households to spent Br. 42.52 additional food on average. That is, the sector made it possible for the participant households to spend more on buying food, and to sustain their augmented food expenditures. Moreover, the mean monthly non-food expenses of the participants and the non-participants are Br. 299.13 and Br. 281.43 respectively, indicating that the mean of non-food expenses of the participants is higher than the control groups and the mean difference between the groups were statistically significant at 5 percent probability level. This can be explained that the contribution to the family non-food expenditure went up with the increase of respondents‘ income.

Here, the study provides evidence as to whether or not the informal sector has brought significant changes on household food consumption and non-food consumption from the participating in the informal sector. The estimation result presented in Table below provides a supportive evidence of statistically significant effect of the sector on household food- consumption and non-food-consumption measured in Birr because of Birr is easily understandable unit. However, the result showed that there is positive and insignificant difference between treated and control in terms of food consumption and non-food consumption.

Table: 4.10 ATT estimation of impact of informal sector participation on food consumption Matching method Number of match ATT Bootstrap T- statistic Participant Non-participant standard error Nearest neighbor 96 139 349.42 784.81 3.65 Kernel 96 139 670.58 826.30 2.19 Radius 96 139 516.96 664.20 5.25 Stratification 96 139 308.71 639.32 3.84

Source: survey result, 2017

Table: 4.11 ATT estimation of impact of informal on non-food consumption Matching Number of match ATT Bootstrap standard T- statistic method Participant Non-participant error

Nearest neighbor 96 139 446.85 45545648.82 3.29 Kernel 96 139 286.93 455.54 2.99 Radius 96 139 302.11 238.81 4.46 Stratification 96 139 155.71 472.38 2.94 Source: survey result, 2017

4.6.5.2 ATT estimation of impact of using informal sector on household income As indicated in the table below ,the number of matched sample observation are 235 in nearest neighbor matching method, in kernel , in caliper or rad matching method, in stratification matching method.

The propensity score matching result shows that, Participant of informal sector does not show significant effect on urban household income in Three of the matching methods with the T-value of 1.075,0.65, and 0.57 in nearest neighbor, caliper or radius matching and stratification matching method respectively. Which is less than t- tabulated (1.96) but Kernel matching method is statistically significant effect on urban household income. Which is greater than t- tabulated (1.96).

Table 4.12: ATT estimation of impacts of informal sector on household income

Matching method Number of match ATT Bootstrap T- statistic Participant Non-Participant standard error

Nearest neighbor 96 139 645.96 502.3 1.075

Kernel 96 139 482.63 403.15 2.35

Radius 96 139 521.56 428.01 0.65

Stratification 96 139 442.97 408.74 0.57

Source: survey result, 2017

4.7 Robust Test

The bootstrapping is done to estimate the standard error. The standard error estimated with bootstrapping is not due to the normal sample variation but also, due to variance in estimation of propensity score, common support and order in which treated individuals are matched. In bootstrapping sample is treated as population and the estimate of sampling distribution is done using the techniques like Monte Carlo technique by drawing the large number of resamples from original sample with replacement (Heckman, Ichimura & Todd, 1998). Under this study the t-statistics were based on bootstrapped standard errors with 100 replications which were used to verify whether the observed effect was significant or not.

Table 4.13 Bootstrap standard error

Observed Bootstrap Normal – based

Coef. Std. Err. Z P>ǀZǀ [95% conf. Interval]

_bs_1 542.284 124.642 3.05 0.000 170.871 891.254

Bootstrap results Number of obs. = 235

Replication = 100

Source: own computation, 2017

4.8 Econometric Analysis

4.8.1 Overall Fitness of the Model

A model specification error can occur when one or more relevant variables are omitted from the model or one or more irrelevant variables are included in the model. It can substantially affect the estimate of regression coefficients. Moreover, in this study, the model specification errors were checked by linktest, the test of hat and hatsq were 0.000 and 0.002 respectively which are significant. Therefore, it shows that the linktest has failed to reject the hypothesis that the model is specified correctly. Accordingly, it seems to us that we don‘t have a specification error (see Appendix).

In addition to the basic descriptive statistics, the logistic regression model was employed to identify the determinants of household poverty in the study area. The variables included in the model were tested for the existence of multi-co linearity, if any. Contingency coefficient and variance inflation factor were used for multi-collinearity test of discrete and continuous variables, respectively (see Appendix).

Contingency coefficient value ranges between 0 and 1, and as a rule of thumb variable with contingency coefficient below 0.75 shows weak association and value above it indicates strong association of variables. The contingency coefficient for the discrete variables included in the model was less than 0.75 that didn‘t suggest multi-collinearity to be a serious concern. As a common practice continuous variable having variance inflation factor of less than 10 are believed to have no multi-collinearity and those with VIF of above 10 are subjected to the problem and should be excluded from the model (Gujarati, 2009).

So as to identify the impact of informal sector to urban poverty reduction in Arba Minch town the dependent variable, probability of being Participate in informal sector was regressed against various explanatory variables. The regression table revealed that binary logistic model managed to predict 32% of the responses correctly.

Apart from percent correct predictions, the model Chi-Square with ―n‖ degrees of freedom and Hosmer and Lemeshow‘s are used to test goodness-of-fit test. Accordingly, p- values associated the Chi-Square with 11 degrees of freedom. The value of 0.0000

63 indicates that the model as a whole is statistically significant that shows the model fit the data well (See Appendix).

Another commonly used test of model fit is the Hosmer and Lemeshow‘s goodness-of-fit test. Hosmer-Lemesshow goodiness-of-fit statistic is computed as the Pearson chi-square from the contingency table of observed frequencies and expected frequencies. Similar to a test of a two- way table, a good fit as measured by Hosmer and Lemeshow‘s test yield a large p-value. Therefore in this study the test result show that p=1 this suggests that the model is correctly fitted with the data (see Appendix)

Robust logistic regression was used to control for hetroscedasticity in binary outcome models. Hetroscedastiscity in binary outcome models will affect both the ―Betas‖ and their standard errors (Wooldridge, 2001) .In this particular study both regression i.e. earlier regression and robust logistic regression have the same result. None of the coefficients changed, but the standard errors and Z values are a little different. Had there been more heteroscedasticity in these data, would have probably seen bigger change. Therefore, this model is free from heteroscedasticity problem (see Appendix).

4.9 Estimation of propensity scores

The logistic regression model was used to estimate the PS of respondents which help to put into practice the matching algorithm between the treated and control groups in the study area. In estimating the PS, data from both groups were pooled such that the dependent variable takes a value of 1 if the household was participant and 0 otherwise.

Estimation results of logit model in Table 4.14 are generally insightful in the case for the entire households where dependent variable is participation in informal sector and the low pseudo-R2 value shows the estimated model appears to perform well for the intended matching exercise. Therefore, it is possible to interpret the model results meaningfully.

Table 4.14: Logit model results of household

Covariate Coef. Std. Err. Odd Ratio Age 0.22*** 0.95 1.25 Gender 0.02 0.47 1.02 Education Level -0.47*** 0.12 0.62 Family Size 0.44*** 0.27 0.64 Saving Status 0.31 0.39 1.03 Migration Status -1.03 0.43 0.35 Remittance Access 0.61*** 0.51 1.84 Health Status -0.66 0.52 0.51 Working Time -0.46 0.13 0.63 Place of work -2.00 0.49 0.13 Cons 4.64 3.10 104.09 Statistics: N 235 LR chi2(10) 103.46 Prob > chi2 0.00 Pseudo R2 0.32 Log likelihood -107.20

Sources: own computation Significant differences are indicated with * p<0.1; ** p <0.05; *** p<0.01

Looking into the estimated model coefficients presented in Table 4.14 among the 10 variables considered in the model, four variables were found to have a significant impact on determining to participation in informal sector and hence poverty (livelihood) status of households at 1% probability levels in the study area. These variables included Age, Educational level, Family Size, and Access to Remittance. This implies that these variables included in the model are simultaneously affecting both the probability of participation decisions into the sector and the outcome variables.

Six of the Ten explanatory variables were found to have no significant influence on household participation and hence livelihood status of households. This means those variables do not strongly explain the participation in the informal sector and this implies that the sector (program) households do not have much distinct characteristics overall. Besides these it showed that the explanatory variables are independent of participation and hence, it becomes easier to find a good match between participant and non-participant households.

Age of has a Positive relationship with household head participation in informal sector and statistically significant at 1% probability level. It implies that as the age of the household head increases, the probability of a household head in informal sector participation tends to increases. It indicates that the relationship between age and being participant in informal sector is linear. A significant number of the informal sector participants were young. This can be explained that the age of sample respondent range from 20-35 years household head it have a power full to run own business because of it have initial capital that gain from family and relatives. The Odd ratio confirm that as the household head age increase by one year, all other variables held at their mean values, (constant) the expected probability of a household head to be participated in informal sector increases by factor of 125. Elias (2015) States that huge proportion 38.33% of Households fall within the age ranges from 26 – 35 years which is productive age group.

Education level of household head was found a significant and Negative determinant of participation to informal sector and statistically significant at 1%. It indicated that relatively individuals who have pursued higher educational level have a Lower likelihood of participating in informal sector than those with less or without any education. The justification for this finding could be that education enhances the society ability to obtain and analyze information that helps them to make appropriate decision about tax, roll and regulation of government. This may probably mean that household head with higher educational level have more exposure to the external environment and information which helps them easily associate to formal sector, government policy and regulations as well as the formal sector procedures than those with less or without any education. From the model result, adding one grade education can decreases the informal sector participant by aforementioned percent that mines by 10 present (see in Appendix). Therefore, for a unit increases in education level, causes the estimated Odds ratio of participating in informal sector decreases by roughly a factor of 62. Similarly Etsubdink (2011) states that number of informal sector participant decrease when education level increases since they have better job opportunities in the formal sector. On the other hand those who are less educated have less opportunity in the formal sector and hence they participate in informal sector. The findings that shows that many of the informal sector participants were literate. Some of these participants may have tried to find a job in the formal sector, but with the high unemployment rate, were unsuccessful, and, thus ended up in the informal sector. It seems logical that a literate

66 person would be able to run a business in the informal sector if they acquire the necessary skills and experience.

Family Size has a significant and Positive relationship within the participation of household head into informal sector. The Positive and the significant relationship indicated that as the number of family members increases, more of them may involve in production activities while others may be idle, their labor could be unproductive. The reason for this may be that household heads with relatively having of more dependents in house who are more likely to exert consumption stress on the household participation in informal sector. This may indicate that households with larger dependents‘ family members‘ cannot accumulate capitals in order to bring out from poverty due to the large portion of their income and output is used to maintain their family consumption. For every increase dependent member in the household, the probability of participating in informal sector decision increases by 10%. Therefore, for a unit number increase in family size, causes the estimated Odds ratio of participating in informal sector increases by roughly a factor of 64.

Access to remittance by the household head significant and positive effect on participation of informal sector at 1% probability level. Our result shows that urban households with access to remittance more participating informal sector than those households with no access to remittance. The justification for this finding could be that the societies who have access to remittance the capacity to produce more and that would enable them to accumulate start-up capital for participation in formal business. the access of remittance increase by one, participation decision of the household increase by 13%.Therefore, the Odds Ratio shows that a unit increase in remittance that causes, the estimated ratio of participating in informal sector increases by factor of 184.

4.10. Sensitivity Analysis of Results

The propensity score matching hinges on the CIA and unobserved variables that affect the participation and the outcome variable simultaneously that may lead to a hidden bias due to which the matching estimators may not be robust. It is not possible to directly reject the CIA. However, Heckman and Hotz (1989) and Rosenbaum (1987) have developed indirect ways of assessing this assumption. These methods rely on estimating a causal effect that is known to be

67 equal to zero. If the test suggests that this causal effect differs from zero, the unconfoundedness assumption is considered less plausible (Imbens, 2004). Sensitivity analysis is aimed to assess the sensitivity of estimated results with respect to deviation from CIA. Thus, Rosenbaum bounds were calculated for program effects that are positive and significantly different from zero.

Table 4.15: Sensitivity analysis

Gamma Outcomes variables = income Consumption expenditure 1.00 0 0 1.25 0 0 1.50 0 0 1.75 0 0 2.00 0 0 2.25 0 0 2.50 1.0e-15 1.0e-15 2.75 1.8e-14 1.8e-14 3.00 2.0e-13 2.0e-13 Source: own computation (Gamma)= log odds of differential due to unobserved factors where Wilcoxon significance level for each significant outcome variable is calculated.

Under the assumption of no hidden bias (log of odd ratio one) for each outcome variables, the upper bound significance levels (sig+ = test statistics) give similar result indicating the significance of treatment effect. For income and consumption expenditure the upper bound on the significance level for gamma value of 1.05 - 3 are 0.00, for all outcome indicators respectively; and implies that the main outcome indicators variables are insensitive to a bias that would multiply the odds of participation by a factor of 1.05 – 3. In conclusion, the results show that the inference for the effect of the program interventions is not changing, though participants and non-participant households have been allowed to differ in their odds of being treated up to 3.00 (200%) in terms of unobserved covariates. Thus, impact estimates of ATT are insensitive to unobserved selection bias. Moreover, the impacts found on all livelihood main outcome indicators do not depend critically on the algorithm used kernel with bandwidth (0.1), since both the value of the coefficients and its significance are very similar using different

68 alternatives such as radius calliper (0.1) and NNM (5). This implies that the impact analysis is reliable and robust; do not depend on one matching estimator. Therefore, impact estimates are insensitive to unobserved selection bias and pure effect on informal sector.

CHAPTER FIVE 5. SUMMARY, CONCLUSION AND RECOMMENDATIONS

5.1. Summary

This study aimed to estimate the determinants of household participation on informal business and its effects on urban poverty in Arba Minch Town. The specific objectives was to assess the socioeconomic and institutional factors which influence the participation of informal sector and estimate the impact of informal sector on urban households poverty (livelihood )indicator variables.

The data were collected using a carefully designed structured questionnaire, with pooled sample households through stratified three stage sampling technique. In doing so, first two sub-city was purposively selected based on averagely many population are there, in compeer to the other sub- city of the town; next, out of 6 kebeles in two sub-city, 4 kebeles were selected randomly and then households were stratified into two stratums and finally in the third stage, using simple random sampling technique 96 participants of informal sector and 139 non-participants of informal sector were selected using probability proportional to size, a total sample of 235 households.

Based on the information on welfare indicator i.e. Adult equivalent consumption we computed poverty line, which is the combination of food and non-food poverty expenditure, Birr 314 i.e. the minimum amount of money required to purchase the consumption bundle in the study area and non-food expenditure ware greater than food expenditure that mean the population who were selected for this study expend more on materials rather than food i.e non- food and food were calculated 138 and 176 respectively.

The poverty incidence, poverty gap and poverty severity were calculated in accordance with the poverty line; and found 0.58, 0.32 and 0.17 percent respectively. Headcount index shows that 58 % of the households were poor and 42 % were not poor, poverty gap result implies 32 % consumption shortfall from the poverty line and severity result indicate 17 % variation among poor households.

To achieve the stated objectives of the study, we applied PSM method to estimate the impact of informal sector on multidimensional poverty (livelihood) aspects of urban households. Since a baseline survey and randomizations are not feasible options in this case, the study is well suited to matching methods. For the purposes of comparison the study presented estimated results with treated and control groups separately.

The logit model result revealed that participation decision is significantly influenced by four explanatory variables: age, access to remittance, family size, and educational level. This implies that households who have aged household head and have access to remittance are more likely to be included in the informal sector than others a n d those who do have high educational level and Family size are less likely to participate in the informal sector than those have less education level and family size.

Finding a reliable estimate of the sector impact necessitates controlling for all such confounding factors adequately. In doing so, PSM has resulted in 76 participant households to be matched with 96 non-participant households using kernel matching estimator with 0.1 band widths. As a result, only 172 sample households were considered in the estimation process after discarding households whose PS values are out of the common support region. The resulting matches passed through many process of matching quality tests such as t-test, reduction in standard bias and chi-square test. Moreover, the computed parametric standard error was bootstrapped in order to capture all sources of errors in the estimates and finally sensitivity analysis was made.

The findings on the ATT shown that clienteles are better off in terms of income and consumption expenditures than non-clienteles and the mean difference was statistically significant in all indicators and positively related to participation in informal sector indicating that the probability of improvement in income and consumption expenditures increases with the increase in participation of informal sector. This show as the mean difference is tax. It implies that treated households show higher income and consumption expenditures improvement than control group. The extent of the mean difference between the groups was substantial as well as statistically significant in all subcomponents of the main indicators.

In case of livelihoods capitals in terms human and social capital, ATT estimation results revealed that the non-participants were found to be better off than participant of informal sector. Expenditures on health care, education and social affairs expenditures considered to be better off for non-participants rather than participants. The mean differences expenditure of the investment between the groups was positive and significant for all subcomponents.

In general in summary, participant‘ households who participant in informal sector are better off in terms of economic wellbeing indicators like income and consumption expenditure measures as well as social wellbeing indicators like human and social capitals.

5.2. Conclusion

Drawing upon a primary provincial-level cross-sectional household survey conducted in Arba Minch town, to analyses the extent of informal sector impact on urban poverty through a range of livelihood aspects that captured and reflected relative economic and social well-being of a typical urban household in the study area. Household livelihoods indicators were captured across two dimensions, the data on which was gathered by administering a structured questionnaire in the field. In order to control for any selection bias that may have arisen during sampling of households, the PSM method was applied, through which ATT finally computed.

Propensity score matching results show that improvement on multidimensional livelihood aspects and have significant positive impact on participated households‘ in terms of all main outcomes indicators. The most prominent and statistically significant differences between the groups were observed in savings, expenditure on education, social affairs, income, expenditure on food and non-food. Overall, non-participant was seen to better in all across which comparisons were made in the final model.

In general it can be concluded that, the empirical result reveals that participation on informal sector has statistically significant and positive impact on household income and consumption expenditure are better off in terms economic and social wellbeing‘s measures. Moreover, the

72 study has confirmed that the impact estimates are insensitive to unobserved selection bias; and also do not depend on one matching estimator

5.3. Recommendations

On the basis of the results of this study, the following recommendations are drawn so as to suggest for the future intervention strategies aimed at the government in the study area in particular, and other similar places in general.

The empirical result from PSM estimation reveals that participation in informal sector has statistically significant and positive impact on household income and consumption expenditure it is better off the government make some policy and regulation for this sector in order to prevent the formal sector from unwanted competition and Government should constrict strategies for informal sector to enhance to transform to formal sector.

Moreover, the findings of this study have several policy implications. The significant impact of informal sector on households‘ livelihood shows more positive signal of importance. When government uses this effect properly, it gains a lot of potential revenues. Furthermore, the savings‘ of household clients increases along with the period of attachment of the clients to the sector. Therefore, this trend of saving behavior should continue so that the participant would be able to expand their business to formal sector. In general, stakeholders who are concerned with informal sector as a means to poverty reduction should take into account the results of this study for better off in participation of informal sector.

Finally, evaluating the overall impact of informal sector on the (poverty) livelihood of urban household by incorporating other remaining components of the framework of tangible and intangible asset of household in the informal sector and environmental effect of informal sector is the other research gap that ought to be addressed in the future. For this reason, the collection of cross sectional data for future research is recommended.

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