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Unemployment in Urban Ethiopia: Determinants and Impact on Household Welfare

Abebe Fikre Kassa

Graduate School Master of Science in Economics Master Degree Project No. 2011:105 Supervisors: Lennart Flood and Yonas Alem

Abstract Data from the 2004 wave of the Ethiopian Urban Socio Economic Survey on four major cities of Ethiopia is used to investigate the determinants of unemployment in urban Ethiopia and its impact on household welfare. Regression results from a binary probit model estimation show that urban unemployment in Ethiopia in 2004 is determined by age, marital status, education beyond primary school and living in the capital Addis Ababa. On the other hand, the results on OLS regression of consumption indicate that unemployment adversely affects household consumption expenditure and hence household welfare. Having one more unemployed household member results in a 4.6 percent decline in per capita real consumption expenditure available to the household. Since unemployment negatively affects household welfare, efforts aiming at reducing unemployment will improve welfare. Quality in the higher education system is believed to reduce unemployment and thereby improving welfare. Mechanisms to reduce household size such as family planning are also recommended.

Key words: urban, unemployment, consumption, welfare, probit, OLS

Acknowledgements I would like to extend my heartfelt thanks to my supervisors Yonas Alem and Lennart Flood for their invaluable pieces of advice and comments on the paper. Special thanks to Yonas for being such an inspiration and of great help throughout my study. I want to thank my family especially my mother who always told me that I will never be troubled in life and will always have caring people around through her prayer. Special thanks to my friends for their support and encouragement. I want to also thank Weyzero Genet and her family in Stockholm for their hospitality when I first came to Sweden. Last, words can not explain my thanks to Selfu (atagna) for always being beside me. (All errors in the paper are my own.)

Table of Contents 1.Introduction ............................................................................................................................. 1 2. Literature Review ................................................................................................................... 4 2.1. Overview of Unemployment and the Ethiopian Urban Labor Market ............................ 4 2.1.1. Unemployment: Causes, Costs and Overview .......................................................... 4 2.1.2. Unemployment in Ethiopia and the urban labor market. .......................................... 6 3.

Econometric Framework ..................................................................................................... 9

4. Data and descriptive statistics .............................................................................................. 12 4.1. The Data ........................................................................................................................ 12 4.2 Descriptive statistics ....................................................................................................... 12 4.2.1. Urban unemployment by age, gender and location: 1994 V 2004.......................... 14 5. Results ............................................................................................................................. 17 5.1 Determinants of Unemployment ................................................................................. 17 5.2 Unemployment and household welfare ...................................................................... 20 6. Conclusions .......................................................................................................................... 24 References ................................................................................................................................ 26 Appendix .................................................................................................................................. 29

1. Introduction Unemployment is one of the challenges facing today’s world. Coupled with population growth and increased poverty, it has a significant impact on growth and development at large. It causes a waste of economic resources such as the productive labor force and affects the long run growth potential of an economy. Unemployment gives rise to private and social problems in the society such as increased crimes, suicides, poverty, alcoholism and prostitution (Rafik et al., 2010 and Eita et al., 2010). High level of unemployment rates can also contribute to the spread of HIV/AIDS in developing countries (Henry et al., 1999 and Astatike 2003). In general, unemployment affects health, household income, government revenue and hence GDP and development at large. Studying unemployment therefore helps tackle these problems through some kind of policy actions. Unemployment is a problem for both developed and developing countries. However, the impact and intensity might differ. According to Rafik et al. (2010), unemployment has been the most consistent problem in both advanced and poor countries. In 2009 for example, as indicated in the World Bank data base (2011), the general unemployment rate (as % of the total labor force) stood at 20.5% in Ethiopia, 23.5% in South Africa, 4.3% in China, 5% in Japan, 9.1% in France, 8.3% in Brazil and Sweden and

9.3% in the US. Recently,

unemployment has increased due to the recent economic crisis of 2007/08 which caused the collapse of aggregate output and led to job cuts. According to IMF (2010) there were about 200 million unemployed people in the world in 2010, 75% of which came from the advanced economies and the rest from emerging economies, and the number has increased substantially since 2007. However, though still high, unemployment in the low income countries declined during the recent crisis. Ethiopia is a poor agrarian country with per capita income of USD350 (World Bank, 2011). Recently, however, the country has been achieving a promising economic growth. According to The Economist (January 6, 2011), the country had the 5th fastest growing economy in the world during the periods 2001-2010 at an average annual GDP growth rate of 8.4% and the 3rd with a forecast of 8.1% during the periods 2011-2015. Despite such improvements, unemployment is high and is one of the socio economic problems in the country. The general unemployment rate (as % of the total labor force) was 20.5% in 2009. It was higher for females (as % of female labor force) at 29.9% compared to males which stood at 12.1%. (World Bank, 2011) 1

The rural population of Ethiopia makes about 83% of the total population but this paper focuses on urban rather than rural unemployment. Even though the urban population makes only about 17% of the total population, its absolute size is big at 15,448,536 (Central Intelligence Agency, 2011). Moreover, most of the educated labor force is concentrated around cities in search of better opportunities and infrastructure, and the rural agricultural sector employs relatively unskilled labor force. The urban sector is also characterized by both skilled and unskilled private sector employment which will all make the analysis of the education effect of unemployment convenient. Another explanation may be that urban unemployment might be more serious than rural unemployment for example in creating political instability. For instance, the recent uprising in the Middle East especially in Egypt and Tunisia which toppled the respective regimes is motivated by major socioeconomic problems such as rising unemployment (Behr and Aaltola, 2011). It is also vital that the obstacles for productivity (which unemployment can be one) should be studied not only in the agricultural sector but also in the urban non-agricultural sector so as for both to contribute for growth and job creation. Unlike most African countries where poverty incidence is relatively higher in rural than urban areas, it is almost similar in Ethiopia. Urban poverty stood at 37% and rural poverty at 45% in 2005 (World Bank, 2005). Growth, unemployment and job creation in urban areas therefore require equal attention for poverty alleviation. Studies addressing urban unemployment in Ethiopia are relatively few. Serneels (2004) studies the nature of youth unemployment and analyzes incidence and duration and concludes that urban youth unemployment for males stands high at 50% in 1994 and mean duration is about 4 years. Duration is shorter for those aspiring for high paying public sector jobs and for those with their fathers as civil servants. Astatike (2003) using data from the 1994 and 2000 waves of the Ethiopian Urban Socio Economic Survey studies the incidence of youth unemployment in Ethiopia with special focus on the urban youth and on the determinants of self-employment in urban Ethiopia (2008) and concludes that youth unemployment was high at more than 50% and self-employment was less among the young, the educated and those who migrated to urban areas recently. Dendir (2000) analyzes the determinants of unemployment duration in urban Ethiopia and concludes that mean duration is 3 years for completed spells and 4.7 years for incomplete spells. Denu et al., (2005/07) in a study on the characteristics and determinants of 2

unemployment, underemployment and inadequate employment in urban Ethiopia, finds that the youth are characterized by relatively high unemployment which differs among the youth group across location, gender and education. . Studies surveyed in this paper are found to mostly concentrate on urban youth unemployment and a few focused on general unemployment. The welfare impact of unemployment is also found to be less explored in the literature at least in the context of Ethiopia. This paper therefore adds to the discussion by focusing on the determinants of unemployment in urban Ethiopia and its impact on household welfare. Specifically it investigates how unemployment behaves over the years 1994-2004. What determines the likelihood of being unemployed in urban Ethiopia in 2004? What is the impact of unemployment on household welfare? The main purpose is answering these questions using household data from the 2004 Ethiopian Urban Socio Economic Survey. I use two econometric methods to answer the research questions: First, with the aim of understanding the determinants of unemployment, I use a binary probit model. Second, to analyze the impact of unemployment on household welfare, I will use ordinary least squares regression technique which estimates household per capita consumption as a function of unemployment and other household factors. The Ethiopian rural labor market is characterized by disguised unemployment (Denu et al., 2005/07). Disguised unemployment exists when few jobs are filled by many people in which case productivity will be low. There is also not much formal employment in rural Ethiopia as most people work in the traditional agricultural sector. Due to these reasons, together with the absence of any rural data in the data set I have, I will not address rural unemployment. The rest of the paper is presented in the following sequence: section two discusses the literature review and section three the econometric framework. Section four discusses the data and descriptive statistics followed by empirical findings. The paper will then conclude with conclusion and recommendations.

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2. Literature Review 2.1. Overview of Unemployment and the Ethiopian Urban Labor Market 2.1.1. Unemployment: Causes, Costs and Overview

The labor market, like any other markets, has both supply and demand sides. The supply side, also called the labor force or the economically active population, has two components: the employed and the unemployed (Hussmanns, 1989). The demand side on the other hand consists of jobs (filled posts) and job vacancies (unfilled posts). According to Olsson (2009), since labor is not a “normal” good we do not have a condition where labor demand equals labor supply at equilibrium wage rate. The prevailing situation in countries around the world is instead the demand for labor is less than the supply due to the higher than equilibrium wage rate and hence there is an excess supply of labor. This gap between the supply and the demand for labor is referred to as unemployment. It is important to understand the causes of unemployment and its consequences for possible intervention. In this section, the causes of unemployment which might slightly differ between developed and developing countries will be discussed. The costs of unemployment will also be discussed briefly. To understand the nature of the labor market in urban Ethiopia, earlier studies on the same will be surveyed. 2.1.1.1 Causes of Unemployment in Developed and Developing Countries

The causes of unemployment are among the extensively debated issues by economists. Keynesian economics stresses on the inadequate aggregate demand in the economy as the major cause. Real wage rigidities and/or real interest rates cause low output and high unemployment. Real wage rigidity, “the failure of wages to adjust until labor supply equals labor demand” according to Mankiw (2002), can cause unemployment. In the real world, wages are set at a higher level than the equilibrium wage rate and the reasons can be considered in three broad views. Efficiency wages theory assumes that higher wages give incentive for workers to exert more effort and reduce shirking. Hence, firms pay higher wages. “The insider-outsider theory” asserts that firms are prevented from cutting wages by labor unions and contracts (Romer, 2005 and Olsson, 2009). The major assumption of this model is that labor unions try to maximize the interests of only their members (the insiders) who are already employed and do not care about non-members(the outsiders). In doing so, firms and the insiders bargain to knock the outsiders out of the job market and thereby create unemployment. Another explanation for higher than equilibrium wages is the 4

search and matching model which emphasizes on the heterogeneity of workers and jobs as the cause for unemployment. Heterogeneity of workers in skills and preferences, information asymmetry and heterogeneity of jobs in their attributes all make it difficult to find the right person for the right job-hence, unemployment. According to Krugman (1994), the welfare system in developed countries particularly in Europe can have an impact on unemployment. Krugman also argues that productivity growth may not come with good employment performance or the vice versa. Increased productivity and employment creation are features of competitiveness and unemployment is part of a decline in economic performance. On technology and unemployment, he asserts that the rapid information and communication technology growth has increased skills premium and possibly played a role in unemployment problem in Europe. Another study by Bassanin and Duval (2006) on unemployment in OECD countries shows that among the determining factors for rising unemployment are high and continuous unemployment benefits, “high tax wedges”, and “stringent and anti-competitive product market regulations”. According to Stiglitz (1974), unemployment in developing countries like those in East Africa is a result of urban to rural migration motivated by the high wage differential. Noveria (1997), on the other hand, states that the major causes of rising unemployment in urban areas in LDCs are education expansion, urbanization which results in rural to urban migration, population growth and job aspiration. For the Ethiopian case, World Bank (2007) indicates that the potential causes of unemployment in urban Ethiopia include the increasing number of the youth labor force, the rising internal migration and literacy rate. Another study by Astatike (2003) states that some of the most important causes in developing countries especially in Ethiopia are the rapidly growing size of the labor force, poor to modest macroeconomic performance, low level of job creation and low level of aggregate demand in the economy. Kingdon and Knight (2004) analyze unemployment in South Africa and they show that unemployment is determined by education, race, age, gender, home ownership and location among others. Echibiri (2005) investigates unemployment in Nigeria using data from 220 randomly selected youths in the city of Umuahia and finds that unemployment is influenced by

age, marital status, dependency ratio, education, current income and employment

preference (paid or self-employment). Eita and Ashipala (2010) study the determinants of 5

unemployment in Namibia for the periods 1971-2007 and conclude that unemployment is positively correlated with investment, wage increase and with an output level below the potential output. They also found that unemployment is negatively related to inflation. Another study by Alhawarin and Kreishan (2010) on long term unemployment in Jordan indicates that age, gender, marital status, region, work experience and education are the major determinants. 2.1.1.2 Costs of Unemployment

Unemployment comes up with costs. According to Feldstein (1997), one who wants to analyze the costs of unemployment should start by disaggregating. The costs of unemployment can be classified broadly as private and social. The private costs of unemployment are those costs borne by the unemployed themselves. The social costs on the other hand refer to those costs to the nation at large and can be the cumulative result of private costs. In this approach, the cost of unemployment can be seen as the opportunity cost of unemployment to the nation i.e., the cost is the national income forgone (Feldstein, 1997 and Astatike, 2003). Unemployment results in a waste of economic resources such as the productive labor force and thereby affect the long run growth potential of the economy. It gives rise to increased crimes, suicides, poverty rates, alcoholism and prostitution (Rafik et al., 2010 and Eita et al., 2010). These evils in turn come up with a cost (cost of crime prevention) and channel resources to their prevention which rather could have been used for other developmental purposes. Unemployment may also have a scary effect. Previous spell in unemployment has a discouraging effect on future participation in the labor force, earnings and welfare in general (Astatike, 2003). Children are affected by the unemployment situation of their parents. According to Dao and Longani (2010), children of jobless parents tend to perform less in their education in the short run. In the long run, a parent’s lost income due to unemployment reduces the child’s earning prospect. Unemployment has an adverse effect on health and mortality via its economic, social and psychological effect on the unemployed. It is also considered as one of the risk factors for HIV/AIDS. 2.1.2. Unemployment in Ethiopia and the urban labor market.

In this section, the Ethiopian labor market and studies on unemployment will be reviewed briefly. The following papers which are conducted on the unemployment situation in Ethiopia 6

will be discussed: Krishnan (1996) on the role of family background for employment, Dendir (2000) on unemployment duration, Astatike (2003) on youth unemployment, Serneels (2004) on the nature of urban youth unemployment, and Haile (2008) on youth self-employment. The World Bank (2007) has also prepared a comprehensive study in two volumes on the urban labor market situation in Ethiopia. The report welcomes the government’s effort towards growth and job creation in the face of increasing poverty and labor supply in urban Ethiopia. Studies addressing urban unemployment in Ethiopia are relatively few and most of those surveyed in this paper concentrate on youth unemployment. Krishnan (1996) studies the role of family background and education on employment in urban Ethiopia and finds that family background (especially father’s education) strongly affects entry to public sector employment but it is not significant in determining entry to lower status private employment. Entry to public sector employment is also affected positively by education while age (being older) positively affects being in the labor force. Dendir (2000) studies unemployment duration in urban Ethiopia and finds that the mean duration is 3 years for completed spells and 4.7 years for incomplete spells. Astatike (2003), using data from the Ethiopian Urban Socio Economic Survey from 1994 to 2000, finds high urban youth unemployment in Ethiopia with more than 50% of the youth unemployed. Between the periods 1994-2000 teen age youth unemployment increased and was higher for women. Those from families of at least secondary school education are found to be affected less according to this study. Serneels (2004), using the 1994 Ethiopian Urban Socio Economic Survey, studies the incidence and duration of unemployment in urban Ethiopia emphasizing on the youth. According to this study, in the year 1994 Ethiopia’s urban unemployment rate was one of the highest in the world with male unemployment standing at 34% and the urban youth unemployment rate was even higher at 50%. Serneels indicates that mean duration of unemployment is 4 years and those youth whose parents are civil servants have shorter durations. It is also indicated that public sector was the top employer hiring one third of the adult men. A negative relationship is found between unemployment incidence and duration, and household welfare. There is evidence that households reduce their savings and consumption to cope with unemployment. With regard to job aspirations, well-educated first time job seekers who aspire to well-paying jobs are more affected. On family background, Serneels concludes that mother’s education may play a role but father’s education has a strong effect for labor market performance in urban Ethiopia. 7

Denu et al. (2005/07) study the characteristics and determinants of youth unemployment and underemployment in Ethiopia from 1984-2001. They conclude that the youth is substantially affected by unemployment and significant differences exist within the youth group across location (urban-rural), gender and education. The urban youth unemployment stood at 7.2% while it was 37.5% for the rural, the latter facing high rate of underemployment. Unemployment for the youth women was 17.3% in 1999 while it was 6.9% for their men counterparts. Regarding education, 44.5% and 32.6% of the unemployed youth were illiterate or had only primary education. The paper indicates that the private sector plays a huge role in employment as a result of policy change by the current government to promote the private sector as opposed to the previous government’s policy where most enterprises were government owned. Using data from the Ethiopian Urban Socio Economic Survey from 1994 to 2000, Haile (2008) studies the nature of self-employment “for the first time in Ethiopia” and finds that the young, the educated, those that migrate to urban areas recently and those without parents in self-employment are less likely to be found in self-employment. The World Bank (2007), with its report in two volumes, acknowledges important improvements in urban unemployment between 1995 and 2005 though the labor market situation remained unchanged. According this study, the rapid rise in the urban labor force creates pressure on the labor market and it can be seen as both a challenge and an opportunity for the Ethiopian government. The rising number of educated labor force entering the market each year as a result of education expansion and internal migration necessitate enhanced job creation in the country. Another feature of the Ethiopian urban labor market indicated in this study is the increasing literacy rate. This is implicated in World Bank (2011) that the net primary school enrollment rate in Ethiopia increased to 87.9% in 2010 from 68.5% in 2005. Low wages characterize Ethiopian urban labor markets although it differs among the type of employers, sector and worker characteristics. Even though females are relatively less skilled yet, the literacy rate and their participation in the labor force is increasing. There is labor market segmentation with a relatively wanted public sector and formal private sector, and a large number of unemployed and a large informal sector with low wages and mostly occupied by women. Women in urban Ethiopia are relatively more affected by unemployment and they are paid lower wages (World Bank, 2007). As can be noted, many of the studies surveyed so far have concentrated on youth unemployment in urban Ethiopia and not many of them focused on general unemployment. 8

The welfare impact of unemployment in urban Ethiopia is also found to be less explored. This paper therefore adds to the literature by focusing on the determinants of unemployment in urban Ethiopia and its impact on household welfare using appropriate econometric techniques. The following section deals with the econometric techniques used to analyze the research questions.

3. Econometric Framework In this section, two models will be specified for analyzing the research questions. First, a binary choice model (probit) estimation technique will be used to analyze the determinants of unemployment. To investigate the impact of unemployment on household welfare, a second model, OLS regression technique will be employed. Model 1: In this first model, the possible determinants of unemployment will be investigated. The main variable of interest is unemployment, a latent variable, where the individual may be classified as either employed or unemployed. The appropriate econometric technique to deal with micro data of this type is using a latent variable approach which can be specified as: (1) Where

is the probability of being unemployed for individual and has a linear relationship

with the possible factors determining unemployment, for the determinants and

.

is a vector of slope parameters

is the stochastic error term which takes care of all the possible

factors determining unemployment which might have not been included in the model. Unemployment is assumed to be a function of household characteristics like age, gender, education, marital status, parental characteristic like parents’ occupation and education, and location. These factors are widely used in most studies that addressed the determinants of unemployment. (Alhawarin and Kreishan, 2010; Bhorat, 2006; Serneels, 2004; Astatike, 2003; Kington and Knight, 2001; Noveria, 1997 and Krishnan, 1996)

The unemployment status of an individual and the possible determinants can not be observed directly but can be inferred from their responses. We can observe the net benefit of the determinants on the probability of getting employed ( 9

) or unemployed (

).

(2) The error term, , has a binomial distribution and its variance conditional on

is:

(3) Using equations (1) and (2), the probability of getting unemployed can be modeled as:

(4) represents a cumulative distribution function (CDF). Maximum likelihood estimation technique can be used to estimate the parameters of binary choice models. For each individual the probability of being unemployed conditional on , i.e., conditional on the individual’s educational level, age, gender, marital status, parents’ occupation, parents’ education and location can be calculated as: {

} {

}

(5)

,

The log likelihood for each individual can then be set as: {

}

}

log{

(6)

There are two commonly used estimation techniques for binary choice models: the binomial probit and binomial logit. For the probit model, the distribution of the cumulative distribution function (CDF),

follows normal distribution and for the logit model, the CDF follows a

logistic distribution. A standard normal distribution has a mean of 0 and a variance of 1 while a standard logistic distribution possesses a mean of 0 and a variance of

2

(Verbeek,

2008). Else, the CDF of both distributions are similar and both estimation techniques yield similar results in applied work. For analyzing the determinants of unemployment in urban Ethiopia, I use probit model. This method is widely used in many literatures addressing unemployment (Cattaneo, 2003).

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In binary choice models, it is difficult to interpret the estimated parameters directly since they tell only the sign of the change in the dependent variable in response to a change in the explanatory variable. Hence, marginal effects have to be calculated. The effect of a change in each determinant on the probability of being unemployed can be found as: (8)

Equation (8) depicts that the effect of a change in a given determinant ( ) on the probability of being unemployed is the product of the effect of the determinant ( ) on the latent variable ( ) and the derivative of the distribution function evaluated at the latent variable (

).

Model 2: Household welfare is assumed to be affected by unemployment situation in urban Ethiopia. The country does not have unemployment benefit system which may imply that most of the unemployed are supported by the employed member in the household. For checking this, a second model will be estimated using ordinary least squares (OLS) estimation technique. The main purpose in here would be to investigate the effect of unemployment on household welfare. The literature says that income and consumption are the two alternative measures of welfare. According to Deaton (1997), in developing countries income is underreported and difficult to remember. So, consumption is used to measure household welfare here and it is modeled as a function of unemployment (the number of unemployed member in the household) and household characteristics.

The OLS regression model in a matrix form can be specified as: (9) Where

is a matrix of determinant variables for consumption expenditure ( ) and

disturbance term with a zero conditional mean (Baum, 2006).

is the

is the coefficient of the

explanatory variables. Equation (9) is also assumed to fulfill all the other classical linear 11

regression assumptions: linearity, independence among the explanatory variables and the error term, and the disturbance terms are uncorrelated and have the same variance. To make the distribution of consumption expenditure more normal

will be used as

the dependent variable. With the absence of unemployment benefit system in Ethiopia, unemployment is expected to have a negative impact on consumption expenditure and hence on household welfare.

4. Data and descriptive statistics 4.1. The Data The data used in this paper is from the 2004 wave of the Ethiopian Urban Socio-Economic Survey (EUSS) collected by Addis Ababa University, Department of Economics, in cooperation with the University of Gothenburg. The data covers 1,500 households from four major cities in Ethiopia-Addis Ababa, Awassa, Mekelle and Dessie. These cities are believed to represent the socioeconomic characteristics of households in urban Ethiopia (Alem and Söderbom, 2011, and Astatike, 2003). The data used for analyzing the determinants of unemployment is individual level and I use household level data for investigating the impact of unemployment on welfare. Summary statistics for unemployment and consumption will be discussed first which will then be followed by the empirical findings. The following table shows the descriptive statistics for the individual level data.

4.2 Descriptive statistics Summary statistics for determinant variables for unemployment and welfare variables are presented in tables 1 and 2 respectively. Table1. Descriptive statistics for the labor force of ages between 15 and 65 Variable (%) Male Female* Age:15-19 Age:20-24 Age:25-29 Age:30-65* Married Others (single, separated, divorced, widowed, too young)* 12

Mean (2004) 51.7 48.3 8 24.3 12.5 44.7 24.7 75.3

Standard Deviation .499 .499 .272 .429 .331 .497 .432

Illiterate* 7.1 Primary school completed 17.9 Junior secondary school completed 21.4 Secondary school completed 37.4 Tertiary school completed 11.4 Mother primary school completed 11 Mother less than primary school completed* 89 Father secondary school completed 11.8 Father less than second. school completed* 88.2 Father working in the private sector 3.8 Father working in the public sector 19 Father working other* 77.2 Living in Addis 83 Living in Awassa 7.1 Living in Dessie 5.5 Living in Mekelle* 4.3 Unemployed 30.9 Employed* 69.1 Tot. obs. 2510 Table continued from the previous page; * denote reference group

.257 .384 .411 .484 .318 .312 .312 .323 .323 .191 .392 .375 .257 .228 .203 .462 .462

From table 1, we can see that there is a fairly equal representation of gender in the sample with men making up 51.7% and females 48.3%. Looking at the age category, the teen age group of the labor force (15-19) constitutes 8%. The age groups 20-24, 25-29 and 30-65 constitute 24.3%, 12.5% and 44.5% of the labor force respectively. 24.7% are married. Looking at the education category, 7.1% of the respondents (heads) are illiterate, 17.9% completed primary school, 21.4% completed junior secondary and 37% have secondary education. Those who completed tertiary education including college diploma, bachelor and postgraduate degree make up 11.4%. Another variable worth looking at is mother’s education. The proportion of those whose mothers have less than primary education is high at 89%. This is not surprising as women in the past were disadvantaged and had relatively less education level in Ethiopia. Father’s education is no exception. 88% of the fathers have less than secondary education and probably that is why only 3.8% of them work in the public sector. As most of the fathers have less than secondary school education, they might not be able to make it to the public sector and to the formal private sector and hence most of them (77.2%) work “other” jobs. The sample consists of more respondents from the capital Addis Ababa (83%). 7.1%, 5.5% and 4.3% of the respondents come from Awassa, Dessie and Mekelle respectively. 13

A study by Astatike (2003) indicates that the urban unemployment rate in Ethiopia stood at 33.3% and 32% respectively in 1994 and 2000. In 2004, where this study is based, unemployment rate stands at 30.9%. The marginal decline may be due to the rapidly growing labor supply driven by population growth and education expansion against the lower absorptive capacity of the labor market, among other possible reasons. The fact that it is declining looks somehow good news but its slow pace is discouraging and urges intervention. For a clear picture of the unemployment situation, the next section briefly overviews unemployment distribution by age, gender and location. 4.2.1. Urban unemployment by age, gender and location: 1994 V 2004

For better understanding of the unemployment situation, this section discusses unemployment disaggregated by age, gender and location. I will also compare the situation in 2004 with 1994 and discuss the changes. The 1994 figures are taken from Astatike (2003) and they cover ages of 15 to 64 while for the 2004 analysis age ranges from 15 to 65. As can be seen from figure 1 below, unemployment rate declined from 33.3% to 30.9% in 2004. On average youth unemployment remains high during both periods. Average unemployment declined in 2004 except for the age group 15-19 which increased by 18 percentage point and the age group 30-65 has lower unemployment rate on average. The rate goes down as one advances to the higher age group. This might be due to the fact that as age increases, people get more education, trainings and experience and hence better employment opportunities. Figure 1: Unemployment rates by age group (1994 and 2004) 80

1994

60 40 20

2004

change

61,7 51,6

54,2 46

38,3

34,5

18

13,5 12,9

0 -20

33,3 30,9

-9,9

-16,4

-4,4

-7,2

-40

How does unemployment differ between men and women? Figure 2 below shows that both in 1994 and 2004 on average female unemployment was higher than male unemployment. The

14

unemployment rate for men has reduced by 12% in 2004 compared to its level in 1994 and by 2.1% for the female category. Figure 2: Unemployment by gender (1994 and 2004) men

women

40 32,5

34,1

33,4

28,6

20 0 1994

-2,1 change -12

2004

-20

Let us now look at city differences in male and female unemployment. It can be read from figure 3 that on average, both male and female unemployment is higher in Addis compared to the other cities. However, this may be the result of the difference in the sample size in Addis (1092 men and 994 women from Addis, table A4 in the appendix) and the possible difference in the education composition of the respondents among others. The female unemployment in Addis is even higher than the average unemployment for the whole sample. On average, there is relatively higher unemployment rate in the capital Addis Ababa (33.4%) and lower unemployment rate in Mekelle (12%). However, this might also be due to the difference in the sample size that covers as many as 2086 from Addis and only 108 from Mekelle (see table A4 in the appendix). Figure 3: Unemployment by gender and location (2004) men

40 35 30

36,5 33,433,4 30,5 28,6

25

women

city total

men total 33,4

33,4 28,6

33,4 28,6

28,6

23,2

23 22 22,5

20

women total

18,8 15,5

15

14

10

10,5 12

5 0 Addis

Awassa

Dessie

Mekelle

Summary statistics for welfare variables is presented in table 2. As can be read, 54% of the households are male headed and 46% are female headed. The average household has 6 members among which one member is unemployed. The dependency ratio stands rather high 15

at 53% which is a burden to the productive labor force in particular and the country in general and hence requires intervention. The larger number of respondents is again from Addis Ababa with 74% coming from the capital and a fairly equal sample is represented from the other cities-8.6% from Awassa, 8.7% from Dessie and 8.4% from Mekelle. The mean per capita real consumption expenditure expressed in 1994 prices, the main variable of interest in this section, is 165 Ethiopian Birr per month although there is a large variation ranging from 11 to as high as 1,754 (also reflected in the high standard deviation of 164.6)1. The sample consists of a few skilled labor force with 31% of the respondents recorded as illiterate and another relatively big number, 27%, having only primary education. When we see the job distribution, 21% work own activity, about 13% work as civil servants, 4.4% for the public sector, 10% in the private sector and 9% as casual workers. Since the sample covers major cities, it is not surprising that fairly many respondents work in the urban formal sector. Table2. Descriptive statistics for welfare variables Variable Real consumption per adult equivalent unit (rconsaeu) Age Household size Dependency ratio (%) Number of unemployed members Male (%) Female (%)* Illiterate (%)* Primary school completed (%) Junior secondary school completed (%) Secondary school completed (%) Tertiary school completed (%) Employer (%) Own activity (%) Civil servant (%) Public sector employee (%) Private sector employee (%) Casual worker (%) Out of the labor force (%) Living in Addis (%) Living in Awassa (%) Living in Dessie (%) Living in Mekelle (%)* No. of Observations *denote reference group 1

In 1994, 1 Dollar was about 5 Ethiopian Birr.

16

Mean 160.1 51.0 6.0 53.3 1.0 53.9 46.1 31.2 27.0 14.8 17.5 9.0 1.0 21.4 12.6 4.4 9.8 9.4 41.0 74.2 8.6 8.7 8.5 1118

Std. Dev. 164.58 14.11 2.69 .59 1.08 .49

.45 .36 .38 .29 .11 .41 .33 .21 .30 .29 .44 .28 .28 .28

With this, let us now proceed to the empirical findings for more technical analysis.

5. Results In this section empirical findings will be discussed. Section 5.1 deals with unemployment where its determinants are discussed and the second part takes care of consumption where the impact of unemployment on welfare is investigated. 5.1 Determinants of Unemployment

In this section, a probit model is estimated for the probability of being unemployed. The dependent variable is unemployment and the explanatory variables are age, gender, marital status, education, mother’s and father’s education, father’s occupation and location (city). All or most of these variables are used in literatures that addressed unemployment (Alhawarin and Kreishan, 2010; Bhorat, 2006; Serneels, 2004; Astatike, 2003; Kington and Knight, 2001; Noveria, 1997 and Krishnan, 1996). The unemployed are defined as those looking for work but unable to find any. Serneels (2004) includes those individuals in the labor force but not looking for work as unemployed with the thinking that in a high unemployment environment people will not sit and wait but they actively look for a job. In this study, however, those “not at paid work and not looking for work” are excluded from the labor force since the strictly unemployed, according to the International Labor Organization (ILO) definition, are those looking for a job and be able to work but unable to find any (Bhorat, 2008). The other obvious categories excluded include students, the disabled, housewives, children and pensioners. In a probit model, it is difficult to interpret regression coefficients directly since the coefficients only tell the direction of change in the dependent variable as a result of change in the predictor variables. Instead, marginal effects are more commonly used. Hence, average marginal effects for each of the explanatory variables are calculated and reported in table 4. Post estimation link test is calculated to check whether the dependent variable (unemployment) is specified correctly. Since the _hat is significant and the _hat squared is not (table 3), the model is correctly specified and no omitted variable exists. The model does not have multicollineraity problem either. (See table A7 in the appendix for detail) Table3: Link Test unemployment _hat _hat squared

17

Coefficient 1.085*** (0.931) .0783 (0.722)

Table4. Determinants of unemployment-Probit regression results. Variable Gender, male

Coefficient Marginal Effects -0.087 -0.0255 (0.058) (0.0170) Age: 15_19 0.845*** 0.2478 (0.103) (0.0286) Age: 20_24 0.773*** 0.2268 (0.069)*** (0.0190) Age: 25_29 0,350*** 0.1028 (0,087) (0.0258) Married -0.483*** -0.1418 (0.081) (0.0234) Primary school completed 0.015 0.0043 (0.117) (0.0358) Junior secondary school completed 0.424*** 0.1244 (0.110) (0.0335) Secondary school completed 0.624*** 0.1832 (0.104) (0.0312) Tertiary school completed -0.289** -0.0847 (0.140) (0.0430) Mother, primary school completed -0.049 -0.0143 (0.093) (0.0279) Father, secondary school completed -0.023 -0.0067 (0.097) (0.0290) Father, working in the private sector -0.174 -0.0512 (0.150) (0.0458) Father, working in the public sector 0.047 0.0137 (0.076) (0.0226) Living in Addis 0.486*** 0.1426 (0.176) (0.0500) Living in Awassa 0.164 0.0480 (0.208) (0.0582) Living in Dessie 0.128 0.0375 (0.218) (0.0652) Log-likelihood -1302.81 -1302.81 Pseudo R2 0.1607 0.1607 Note: ** significant at 5%; *** significant at 1%; standard errors in brackets

Reading from table 4, compared to the age group 30-65 all the other age groups are positively associated with unemployment. However, the marginal effect declines as age increases. For the teen age group for instance, an additional year in the age of the head from the mean is associated with a 24.8% increase in the likelihood of being unemployed. The same situation results in an increase in the probability of getting unemployed by 22.7% and 10.3% for the

18

age groups of 20-25 and 25-29 respectively. This is consistent with the finding by Serneels (2004) for the youth. For the education variable, the result reveals that up to the education level of secondary school, one is likely to be unemployed as the level of education increases, consistent with Serneels (2004) for the urban youth. Contrary to Serneel’s finding, however, tertiary education is significantly and negatively associated with the likelihood of being unemployed. This is also consistent with the finding by Bhorat (2008) for South Africa. Those with tertiary education are 9% less likely to be unemployed compared to the illiterate. This is because people with tertiary level of education have better job opportunities since they are skilled. Primary education is insignificant and this may be due to the fact that in urban areas, there is relatively lower demand for unskilled labor force. Contrary to the finding by Krishnan (1996) and Serneels (2004), parent’s education and occupation are insignificant in determining unemployment. As mentioned in the descriptive statistics earlier, 89% of the mothers have education level of less than primary school and that may be why mother’s education could not play a role in defining unemployment. The same logic applies to father’s education among which 88% have less than secondary education. Location is another variable that determines unemployment. Contrary to the finding by Serneels (2004) for the youth but consistent with Bhorat (2008) for South Africa, living in the capital Addis Ababa is associated with high probability of being unemployed. On average people living in Addis have a relatively 14.3% higher probability of being unemployed compared to those in Mekelle. This could be due to congestion caused by the absolute size of people living in the metropolitan looking for better opportunities. This may also be due to the absolute size of the sample (2086 in Addis). There is a negative association between getting married and being unemployed. This is consistent with the finding by Krishnan (1996). Looking at the marginal effect, married people have a 14.7% less probability of being unemployed. It may not be the case that when people get married, they have better likelihood of getting employed. Instead, it may be that they strive to find a job before getting married as marriage is believed to come up with responsibilities and most people get married after securing some source of income for future life or looking for one after getting married.

19

In sum, unemployment in urban Ethiopia in 2004 is found to be determined by age, marital status, education above primary school and living in the capital Addis. As for the other variables, gender, parental characteristics like mother’s and father’s education and occupation are insignificant in determining unemployment, all things remaining the same. However, even though insignificant, the signs of their estimated coefficients meet expectation. The probability of unemployment: decreases for a male, decreases for those whose mothers have at least primary education and whose fathers’ completed secondary school and for those whose fathers work in the private sector. In the following sections, the impact of unemployment on household welfare will be investigated. 5.2 Unemployment and household welfare

In this section, OLS regression model is estimated for consumption with the main objective of investigating the impact of unemployment on consumption expenditure and hence on household welfare. To account for the size of the household and its composition, household consumption expenditure per adult equivalent rather than aggregate consumption is used and transformed into log form (Alem and Söderborm, 2011). The dependent variables used are age, age squared divided by 1000 (to make the number manageable), household size, number of unemployed members in the household (which captures unemployment), dependency ratio (the ratio of the labor force to those out of the labor force), gender, education and location. Some or all of these variables are used in studies that addressed consumption (Alem and Söderborm, 2011, and Bigsten and Shimeles, 2005). Table 7 presents the results from OLS for log real consumption per adult equivalent unit. In a single log-model like this one, the estimated coefficients are semi elasticities measuring the percentage change in the dependent variable as a result of a unit change in the predictor variable, keeping all others constant. Robust standard errors are used to take care of heteroskedasticity. Ramsey RESET test is performed on each of the predictors to check for any omitted variables bias. The result below shows that the null hypothesis of no misspecification can be accepted at 5% significance level (since P(F)>5%) and it can be concluded that the model is fitted well and there are no omitted variables.

20

Table5. RESET Test Ramsey RESET test using powers of the fitted values of log real consumption per capita Ho: model has no omitted variables F(3, 1094) = 1.68 Prob > F = 0.1686 Multicollinearity may inflate standard errors. However, as long as there is no perfect multicollinearity (which the stata software detects automatically) the regression estimates will not be biased. To check whether perfect multicollinearity is a problem, variance inflated factors (VIF) are calculated and presented in table 6. If the highest variance inflation factor is greater than 10, there is evidence of collinearity. But, near collinearity that doesn’t influence the main variable of interest can be ignored (Baum, 2006). As can be noted from table 6, age and age squared have a VIF of greater than 10 and since they are not the main concern here and since the exclusion of one of them do not influence the result, I ignore their high VIF. Because the VIF of all other explanatory variables is less than 10, it can be concluded that multicollinearity is not a problem in the data. Table6. Variance Inflation Factors Variable age agesq hhs hhssq addis awassa dessie secondary junsec tertiary primary civil male unempmb casual ownacct private public depratio employer Mean VIF

VIF 35.84 35.09 9.28 8.57 2.75 1.95 1.89 1.84 1.64 1.61 1.56 1.54 1.43 1.38 1.37 1.36 1.33 1.19 1.16 1.06 5.69

1/VIF 0.027899 0.028497 0.107712 0.116650 0.363709 0.513036 0.527900 0.542948 0.610689 0.619901 0.641548 0.648250 0.697707 0.725883 0.727481 0.735782 0.752856 0.837146 0.860047 0.942790

21

Table7. Determinants of log real per capita consumption- OLS regression results Robust Std. Err. Age .008 .009 Age squared/1000 .001 .988 Household size -.192*** .022 Household size squared .008*** .001 Dependency ratio -.084** .036 Gender, male .015 .046 Primary school completed .152*** .056 Junior secondary school completed .413*** .068 Secondary school completed .612*** .070 Tertiary school completed .873*** .086 Employer .379* .218 Own activity .007 .056 Civil servant -.103 .067 Public sector employee .053 .109 Private sector employee -.055 .075 Casual worker -.269*** .069 Living in Addis -.079 .075 Living in Awassa .081 .095 Living in Dessie -.368*** .092 Number of unemployed member -.046** .021 Intercept 5.022*** .230 Note: * significant at 10%; ** significant at 5%; *** significant at 1% Variable

Coefficient

Consistent with the finding by Alem and Söderborm (2011), the result in table 7 indicates that the larger the household size, the less the real consumption expenditure per adult equivalent will be, keeping all other variables constant. One more household member results in a 19% decline in the real per capita consumption expenditure available to the household. The dependency ratio since it is the ratio of people out of the labor force to those in the labor force, simple logic tells us that the higher the dependency ratio, the less per capita consumption in a household. The results confirm this. A one unit increase in the dependency ratio decreases the real consumption per adult equivalent by about 8%. Education is observed to strongly increase real per capita consumption expenditure, consistent with Alem and Söderborm (2011). Keeping all other variables constant, those households with the head having tertiary education have 8.6% higher real consumption expenditure per adult equivalent compared to the ones with no education. This may be due mainly to the

22

income effect of education. Better education is likely to increase income which in turn increases consumption. Occupation of the head is also one of the factors affecting consumption expenditure. Being an employer, for instance, means a relatively better income and hence better consumption expenditure. The result indicates that families with heads working as employers have 37.9% higher real consumption expenditure per adult equivalent. The result on the head working as casual worker confirms the finding by Alem and Söderborm (2011) that relatively speaking, households with heads working as casual workers have less consumption expenditure. These households have 27% less real per capita consumption expenditure compared to those working other jobs. There is no evidence that location matters for consumption except that those living in Dessie have 36.8% less real consumption per capita expenditure compared to the ones in Mekelle. Since there is no unemployment benefit system in Ethiopia, it is highly likely that the burden of the unemployed member rests on the shoulder of the household. This in turn affects consumption expenditure and hence household welfare. Accordingly, one more unemployed member in the household results in a 5% decline in the real consumption expenditure per adult equivalent. This goes with the expectation in the beginning of this paper that unemployment has a negative impact on consumption and hence on welfare. Age of head, age of head squared/1000, gender, working own activity, working as civil servant, public, private employment, Addis and Awassa city dummy variables are not significant in determining real consumption expenditure per adult equivalent.

23

6. Conclusions In this study the determinants of unemployment in urban Ethiopia and its impact on household welfare is investigated using data from the 2004 wave of the Ethiopian Urban Socio Economic Survey on four major cities-Addis Ababa, Awassa, Dessie and Mekelle. Comparison of the unemployment situation by age, gender and location has also been made for the periods 1994 and 2004. 30.9% of the Ethiopian urban labor force was unemployed in the year 2004. The rate slightly decreased from its level of 33.3% a decade ago. Both 1994 and 2004 data have witnessed high female unemployment rates on average although the rates have declined in 2004. Teen age unemployment is high at 54.2% and increased by 18 percentage point in 10 years. Given the relatively larger sample size, Addis is characterized by higher average unemployment for almost every age group and gender compared to the other cities. Probit model estimation technique is employed for the purpose of understanding the determinants of unemployment. The evidence indicates that the factors determining urban unemployment in Ethiopia are age, marital status, education above primary school and living in Addis. The likelihood of unemployment increases with age, taking ages of 30 to 65 as reference. Heads with education levels up to secondary school have relatively higher probability of being unemployed and those with tertiary education have 8.7% less probability of getting unemployed. Living in the capital Addis Ababa is associated with high probability of being unemployed which may be due to the relatively larger sample size used. Another possible explanation could be the increased pressure on the labor force caused by the rising population size in the capital. The result also shows that married people are 14.7% less likely to be unemployed. A second model, OLS regression, is estimated for log real household consumption expenditure per adult equivalent. The result shows that the factors determining consumption expenditure in urban Ethiopia are household size (negatively), dependency ratio (negative), education (positive), being an employer (positive), casual work (negative) and the number of unemployed members in the household which captures unemployment. With the absence of unemployment benefits in Ethiopia, the evidence indicates that unemployment has a negative impact on household consumption expenditure and hence on household welfare. One more unemployed household member decreases household consumption expenditure by 5%.

24

Since unemployment adversely affects household welfare via its impact on consumption, every effort to reduce unemployment will be translated into welfare. If the problem of unemployment can be reduced, welfare will improve in a way. The following recommendations therefore intend both to reduce unemployment and improve welfare. Efforts being exerted for alleviating poverty in the country will come up with short term and long term employment opportunities. If such policies and strategies are implemented successfully, welfare will improve. Improving urban infrastructure will also create short term and long term employment opportunities and thereby improve welfare, all other things remaining the same. Migration from rural to urban areas puts pressure on the urban labor force and most casual workers in Ethiopia come from rural to urban areas in search of better opportunities. It is thus vital to design policies aiming at reducing rural urban migration like improving rural infrastructure to reduce the pressure on the urban labor force and thereby improve welfare. The increasing number of schools, universities and colleges is good news for the country in terms of producing skilled and creative labor force. However, the quality of education remains low (World Bank, 2003 and USAID, 2007). Quality of the education system should be worked hard on to produce creative and able labor force. It is found that households with the head working as employer have a better per capita consumption and hence welfare. . Corruption according Norris (2000) has a discouraging effect on the activities of the private sector. Strengthening the private sector can potentially increase “employer” household heads. Entrepreneurship and self-employment can create new jobs and so as to rip the best out of them, the major bottlenecks for the success of such practices should be fought-corruption and bureaucracy. Since tertiary education decreases unemployment, there should be enhanced effort on skill and employment creation for the skilled labor force. Family planning awareness may aid in reducing household size which adversely affects welfare. Education quality is also believed to aid in reducing dependency since it will reduce unemployment.

25

References Alem, Y. and Söderborm, M. (2011). Household-Level Consumption in Urban Ethiopia: The Effects of a Large Food Price Shock. World Development. Alhawarin, I.M. and Kreishan, F.M. (2010). An Analysis of Long-Term Unemployment (LTU) in Jordan’s Labor Market. European Journal of Social Sciences, 15(1), 56-65 Bassanini, A. and Duval, R. (2006). The Determinants of Unemployment Across OECD Countries: Reassessing the Role of Policies and Institutions. OECD Economic Studies, (42) Baum.F. C., (2006). An Introduction to Modern Econometrics Using Stata. A Stata Press Publication: Stata Corp LP, College Station, Texas. PP 69-72; 247-255. Behr, T. and Aaltola, M. (2011). The Arab Uprising. Causes, Prospects and Implications. FIIA Briefing Paper 76. Bhorat, H (2006). Unemployment in South Africa: Descriptors and Determinants. Paper presented to the Commission on Growth and Development, World Bank, Washington DC. Bigsten, A. and Shimeles, A. (2005). Poverty and Income Distribition in Ethiopia: 1994-2004. Department of Economics, Göteborg University. Cattaneo, C (2003).The Determinants of Actual Migration and the Role of Wages and Unemployment in Albania: an Empirical Analysis. Università degli studi di Milano Central Intelligence Agency, The World Factbook. http://www.cia.gov/library/publications/the-world-factbook/geos/et.html. Accessed 2011-06-04 Deaton, A (1997). The Analysis of Household Surveys: A Microeconomic Approach to Develpment Policy. Baltimore: Jhons Hopkins University Press. Dendir, S. (2006). Unemployment Duration in Poor Developing Economies: Evidence from Urban Ethiopia. The Journal of Developing Areas, R23, J64, O55. Denu, B., Tekeste A., and Deijl V.D.H. (2005/07). Characteristics and determinants of youth unemployment, underemployment and inadequate employment in Ethiopia.

26

Employment Strategy Papers. Eita, J. H. and Ashipala, J. M. (2010). Determinants of Unemployment in Namibia. International Journal of Business and Management, 5(10), 92-104. Feldstein, M. S. (1997). The Private and Social Costs of Unemployment. Paper presented at the American Economic Association Meeting. Working Paper 223. New York. Haile, G. (2003). The incidence of youth unemployment in urban Ethiopia. Paper Presented at the 2nd EAF International Symposium on Contemporary Development Issues in Ethiopia, Addis Ababa, Ethiopia, 11-13 July, 2003. Haile, G. A. (2008). Determinants of self employment in urban Ethiopia: Panel data based evidence. Policy Studies Institute Discussion Papers, (906). Henry, V., Penalba, C., Beguinot, I. and Deschamps, F. (1999). Relationships between work and HIV/AIDS status L. Occup. Med.49(2), 115-116. Hussmanns, R. (1989). Measurement of employment, unemployment and underemployment– Current international standards and issues in their application. Bulletin of Labour Statistics, ILO, Geneva, 1989-1 Kingdon, G.G. and Knight, J. (2004). Unemployment in South Africa: The Nature of the Beast. Krishnan, P. (1996). Family Background, Education and Employment in Urban Ethiopia. Oxford Bulletin of Economics and Statistics, 58(1), 167-182. Krugman, P. (1994). Past and Prospective Causes of High Unemployment. Federal Reserve Bank of Kansas City. Mankiw, N.G. (2002). Macroeconomics, 5th Edition. New York, NY: Worth, 2002. Norris, E. D. (2000). A Game Theoretic Analysis of Corruption in Bureaucracies. IMF Working Paper, Fiscal Affairs Department Noveria, M. (1997). Unemployment in Less Developed Countries: Patterns, Causes and Its Relationship to the Problems of Poverty. Buletin Pengkajian Masalah Kependudukan dan Pembangunan, VIII (2 &3) 1997. Olsson, O. (2009). Essentials of Advanced Macroeconomic Theory. 27

Rafik, M., Ahmad, I., Ullah, A. And Khan, Z. (2010). Determinants of Unemployment: A Case Study of Pakistan Economy (1998-2008). Abasyn Journal of Social Sciences, 3(1) Romer, D. (2005). Advanced Macroeconomics, 3rd Edition. Boston: McGraw-Hill, 2005. Serneels, P. (2004). The Nature of Unemployment in Ethiopia. CSAE WPS/2004-01. Stiglitz, J. (1974). Alternative Theories of Wage Determination and Unemployment in LDC’s: The Labor Turnover Model. The Quarterly Journal of Economics, Oxford University Press, 88(2), 194-227. The Economist Online, (2011). Africa’s Impressive Growth. http://www.economist.com/blogs/dailychart/2011/01/daily_chart?fsrc=scn/fb/wl/ar/dailychart africa, (Accessed April 18, 2011) USAID, (2007). President's International Education Initiative Expanded Education for the World's Poorest Children Ethiopia Fact Sheet. http://www.usaid.gov/press/factsheets/2007/fs070924_5.html, (Accessed June 05, 2011) Verbeek, M. (2008): A Guide to Modern Econometrics, 3rd Edition. Hoboken, N.J.; Chichester: Wiley, 2008. World Bank, (2011). Ethiopia: Country Brief. http://web.worldbank.org/WBSITE/EXTERNAL/COUNTRIES/AFRICAEXT/ETHIOPIAEX TN/0,,menuPK:295939~pagePK:141132~piPK:141107~theSitePK:295930,00.html (Accessed April 18, 2011) World Bank, (2011). Ethiopia Protection of Basic Services Phase 2 Project. http://web.worldbank.org/WBSITE/EXTERNAL/COUNTRIES/AFRICAEXT/ETHIOPIAEX TN/0,,contentMDK:22886509~menuPK:50003484~pagePK:2865066~piPK:2865079~theSite PK:295930,00.html. (Accessed April 26, 2011) World Bank, (2007). Ethiopia: Urban Labour Markets in Ethiopian: Challenges and Prospects. Synthesis Report. Vol I & II. World Bank, (2003). Higher Education Development for Ethiopia. A World Bank Sector Study World Bank, (2011). World Development Indicators: http://data.worldbank.org/indicator/SL.UEM.TOTL.ZS (Accessed April 18, 2011)

28

Appendix Note: * taken from Astatike (2003);** and *** for 1994, age covers 15-64 and 16-65 for 2004. The 2004 figures are own calculations. TableA1. Unemployment rates by age group. 1994*

2004

change

Age:15-19

46%

54.2%

18.0%

Age:20-24

61.7%

51.6%

-16.4%

Age:25-29

38.3%

34.5

-9.9%

Age:30-64/65**

13.5%

12.9%

-4.4%

Age:15-64/65***

33.3%

30.9%

-7.2%

TableA2. Unemployment rates for men Age

1994*

2004

change

15-19

55.7%

58%

20-24

61.9%

25-29

TableA3. Unemployment rates for women Age

1994*

2004

4.1%

15-19

40.2%

51.5% 28.1%

48.3%

-22.0%

20-24

61.5%

54.7% -11.1%

40.8%

34.7%

-15.0%

25-29

35.8%

34.3% -4.2%

30-64/65**

13.8%

13%

-5.8%

30-64/65**

13%

12.8% -1.5%

15-4/65***

32.5%

28.6%

-12.0%

15-64/65***

34.1%

33.4% -2.1%

29

change

TableA4. Unemployment rates by gender and location Male

Female

City Total

Freq

Percent Freq Percent

Freq

Percent

Addis

1092

30.5%

994

36.5%

2086

33.4%

Awassa

87

23%

91

22%

178

22.5%

Dessie

69

15.5%

69

23.2%

138

18.8%

Mekelle

50

14%

57

10.5%

108

12%

TableA5. Unemployment by age group and location Age: 15-19

Age:20-24

Age:25-29

Age:30-65

Freq

Percent

Freq Percent

Freq

Percent

Freq

Percent

Addis

171

56.1%

525

53.5%

272

36.4%

891

14.8%

Awassa

13

53.8%

42

42.9%

20

35%

83

1.2%

Dessie

12

25%

27

40.7%

10

20%

83

8.4%

Mekelle

5

60%

16

31.3%

11

-

65

7.7%

30

TableA6. Unemployment by age group, gender and location Age

15-19 Male

Gender

Female

Male Freq

25-29

Female

Male

Perc.

Freq

Perc

Freq

275

30-65 Female

Male

Female

Perc.

Freq

Perc.

Freq

56.7% 147

36.1%

125

36.8% 509

14.9% 382

14.7%

55.6%

11

18.2% 43

2.3%

40

?

Freq

Percent

Freq

Perc.

68

57.4%

103

55.3% 250

50%

Awassa 6

66.7%

7

42.9% 19

36.8% 23

47.8% 9

Dessie

50%

8

12.5% 15

33.3% 12

50%

5

5

40%

45

6.7%

38

10.5%

66.7%

2

50%

50%

20%

6

5

?

30

6.7%

34

8.8%

Obs city

20-24

Addis

4

Mekelle 3

6

10

1

Perc.

Freq

Perc.

Table A7. Correlation matrix (unemployment) unemp04 male

age15_19 age20_24 age25_29 married primary junsec

second~y tertiary

addis

awassa

dessie

unemp04

1.0000

male

-0.0523

1.0000

age15_19

0.1488

-0.0674 1.0000

age20_24

0.2541

-0.0473 -0.1672

1.0000

age25_29

0.0293

0.0124

-0.1114

-0.2139

1.0000

married

-0.2357

0.1549

-0.1556

-0.2366

-0.0990

1.0000

primary

-0.1194

-0.0098 0.0266

-0.0735

-0.0349

0.1268

1.0000

junsec

0.0770

0.0171

0.0856

0.0753

0.0321

-0.0272

-0.2441

1.0000

secondary

0.2049

0.0543

-0.0307

0.0750

0.0599

-0.1395

-0.3610

-0.4035 1.0000

tertiary

-0.1504

0.0454

-0.0827

-0.0570

0.0165

0.0327

-0.1676

-0.1873 -0.2770

1.0000

addis

0.1198

0.0282

0.0155

0.0447

0.0382

-0.1235

-0.0415

0.0541

0.0412

awassa

-0.0505

-0.0157 -0.0072

-0.0046

-0.0103

0.0214

0.0368

-0.0308 -0.0145

-0.0014 -0.6128 1.0000

dessie

-0.0630

-0.0083 0.0061

-0.0266

-0.0381

0.0966

-0.0307

-0.0365 -0.0093

-0.0425 -0.5350 -0.0666 1.0000

2

0.0669

1.0000

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Unemployment in Urban Ethiopia: Determinants and Impact on Household Welfare Abebe Fikre Kassa Graduate School Master of Science in Economics Master...

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