The impact of education to the transition from unemployment to employment in Egypt

Gad, Amany Yashoa Article The impact of education to the transition from unemployment to employment in Egypt Review of Economics and Political Science (REPS) Provided in Cooperation with: Cairo University, Cairo Suggested Citation: Gad, Amany Yashoa (2025) : The impact of education to the transition from unemployment to employment in Egypt, Review of Economics and Political Science (REPS), ISSN 2631-3561, Emerald, Bingley, Vol. 10, Iss. 1, pp. 16-33, https://doi.org/10.1108/REPS-07-2020-0093 This Version is available at: https://hdl.handle.net/10419/316131 Standard-Nutzungsbedingungen: Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden. Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen. 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If the documents have been made available under an Open Content Licence (especially Creative Commons Licences), you may exercise further usage rights as specified in the indicated licence. https://creativecommons.org/licenses/by/4.0/ The impact of education to the transition from unemployment to employment in Egypt Amany Yashoa Gad Misr University for Science and Technology (MUST), 6th of October, Egypt Abstract Purpose –This paper aims to identify the level of contribution of different levels of education to remaining in unemployment as well as the transition from unemployment to employment in Egypt. Design/methodology/approach –In this paper, transition probabilities matrix differentiated by gender, age groups, educational levels, marital status and place of residence based on worker flows across employment, unemployment and out of labor force states during the period 2012–2018 using Egypt Labor Market Panel Survey of 2018. The results point to the highly static nature of the Egyptian labor market. Employment and the out of labor force states are the least mobile among labor market states. This is because employment state is very desirable and the out of labor force is the largest labor market states, especially for females. Also, this study examines the impact of different educational levels separately on remaining in unemployment and transition from unemployment to employment state using eight binary logistic regression models. Findings –The main results of transitions from unemployment to employment are relatively large for males, elder-age, uneducated workers as well as workers who are not married and urban residents, and the results of the logistic regression models consistent with the transition probabilities matrix results, except for few cases. Based on the above findings, there is enough evidence to accept the null hypothesis that no education has a positive significant impact to transition unemployed individuals from unemployment to employment, while less than intermediate as well as higher education have a negative significant impact to transition unemployed individuals from unemployment to employment. Originality/value –This paper proposes to address the problem of the unemployment among highly educated which is much higher compared with illiterates and try to understand the impact of different levels of education separately on the transition from unemployment to employment, to help the policymakers to eradicate the gap between education and the demand of the labor market in Egypt. Keywords Education, Employment status, Transition probabilities matrix analysis, Binary logistic regression, Egypt Paper type Research paper 1. Introduction The problem of unemployment is a global problem that affects the world’s economies, and sometimes it is called the body of the economy. In the early literature, the issue of the relation between unemployment and human capital, especially education, is studied in the static analysis, but recently by using the panel data, the dynamic analysis is available. Unemployment is a waste of society’s human capital. Human capital played the main role in the unemployment duration and labor mobility. Unemployment is the most important challenge facing policymakers in the world, especially in developing countries. Unemployment duration is higher for people with higher educational levels, which shows REPS 10,1 16 © Amany Yashoa Gad. Published in Review of Economics and Political Science. Published by Emerald Publishing Limited. This article is published under the Creative Commons Attribution (CC BY 4.0) licence. Anyone may reproduce, distribute, translate and create derivative works of this article (for both commercial and non-commercial purposes), subject to full attribution to the original publication and authors. The full terms of this licence may be seen at http://creativecommons.org/licences/by/4.0/ legalcode The current issue and full text archive of this journal is available on Emerald Insight at: https://www.emerald.com/insight/2631-3561.htm Received 15 July 2020 Revised 20 November 2020 Accepted 12 August 2021 Review of Economics and Political Science Vol. 10 No. 1, 2025 pp. 16-33 Emerald Publishing Limited e-ISSN: 2631-3561 p-ISSN: 2356-9980 DOI 10.1108/REPS-07-2020-0093 that less educated people have lower wages; a similar result was found for females and males. The previous results related to the study conducted by Canavire-Bacarreza and Soria (2007), using data from the household surveys in Argentina for the period 1998 to 2005. This study applied “stochastic dominance and econometric techniques”as well as “conditional multinomial probity techniques.” In Egypt, the unemployment rate has slightly decreased from 12.7% in 2012 (9.3% for males vs 24.1% for females) to about 11.8% in 2017 (8.2% for males vs 23.1% for females), the unemployment rate is high for females compared with males over 2012–2017, while it is slightly decreased from 24.1% to 23.1% during this period. The unemployment rate is high especially among the highly educated, about 33.2% in 2012 and about 34% in 2017, while it is very low among illiterates with about 3.2% in 2012 and 3% in 2018 (CAPMAS, 2019). It is clear that unemployment among highly educated is much higher compared with illiterates and it slightly increased during this period, while it is very low among illiterates and slightly decreased during the same period. Due to this issue, it is useful to address this problem and try to understand the impact of different levels of education separately on the transition from unemployment to employment, to help the policymakers to eradicate the gap between education and the demand of the labor market in Egypt. This study proposes to address the problem of the unemployment among highly educated which is much higher compared with illiterates and try to understand the impact of different levels of education separately on remaining in unemployment and transition from unemployment to employment, to help the policymakers to eradicate the gap between education and the demand of the labor market in Egypt. In this study, the transition probabilities matrix is differentiated by gender, age groups, educational levels, marital status and place of residence based on worker flows across employment, unemployment and out of labor force states during the period 2012–2018 using Egypt Labor Market Panel Survey of 2018. Also, this study examines the impact of different educational levels separately on remaining in unemployment and on the transition from unemployment to employment using binary logistic regression models. To sum up, the main findings of transitions from unemployment to employment are relatively large for males, elder-age, uneducated workers as well as workers who are not married and urban residents, and the results of the logistic regression models consistent with the transition probabilities matrix results, except for few cases. Based on the above findings, there is enough evidence to accept the null hypothesis that no education has a positive significant impact to transition unemployed individuals from unemployment to employment, while less than intermediate as well as higher education have a negative significant impact to transition unemployed individuals from unemployment to employment. The plan of the paper is organized as follows. The rest of this section contains the objectives and research hypothesis. Section 2 presents the literature review. Section 3 describes the data source. Section 4 describes the methodology. Section 5 discusses the results of our analysis, and Section 6 presents the conclusion and recommendations. 1.1 Objectives of the study The objectives of this study are to (1) Examine the relationship between education levels (primary education, secondary education, and higher education) and remaining in unemployment in Egypt. (2) Examine the relationship between education levels (primary education, secondary education, and higher education) and transition from unemployment to employment in Egypt. Transition from unemployment to employment 17 (3) Identify the level of contribution of different levels of education to remaining in unemployment in Egypt. (4) Identify the level of contribution of different levels of education to transition from unemployment to employment in Egypt. 1.2 Research hypothesis The null hypotheses based on these objectives are the following: H0(1). No education has a significant impact on remaining in unemployment in Egypt. H0(2). Less than intermediate education has a significant impact on remaining in unemployment in Egypt. H0(3). Intermediate and above education has a significant impact on remaining in unemployment in Egypt. H0(4). Higher education has a significant impact on remaining in unemployment in Egypt. H0(5). No education has a significant impact on the transition from unemployment to employment in Egypt. H0(6). Less than intermediate education has a significant impact on the transition from unemployment to employment in Egypt. H0(7). Intermediate and above education has a significant impact on the transition from unemployment to employment in Egypt. H0(8). Higher education has a significant impact on the transition from unemployment to employment in Egypt. 2. Literature review Several studies examine the dynamic labor market using Markov transition process probabilities. The pioneering studies are conducted by Funkhouser (1997). Other studies of labor mobility include the study of Gong et al. (2004) that examine mobility patterns and their dynamics about individual characteristics in five cities in Mexico and the study of Tansel and Kan (2012) in Turkey. A study conducted by Gong et al. (2004) explore the labor mobility in five urban cities of Mexico using two separate five-wave panels over the period 1992–1995. The aim is to determine the mobility patterns and their underlying dynamics associated with individual characteristics. This study finds the probability of transitions between not working and the informal sector to be higher than that between not working and the formal sector. Also, this study notes that probability of remaining in the formal sector is significantly higher than that in informal sector. Then, “a reduced form dynamic multinomial panel logit model with random effects is run to examine the influence of one’s age, education, gender, ethnicity, region and previous labor market state.”The results reveal a positive relationship between education level and the formal sector employment, a negative (positive) relationship between the income of other family members, and informal sector employment. A study conducted by Bernab e and Stampini (2009) examines the relationship between unemployment and higher education using quarterly 1998–1999 panel data to examine mobility across six labor market statuses (inactivity, unemployment, formal wage employment, informal wage employment, self-employment and farming). The study REPS 10,1 18 concluded that there is “largely a queuing device for individuals with higher education waiting for formal jobs. Some self-employment is subsistence activities and consistent with a segmented labor market, while other is high risk and potentially high return activities.”Age, gender and education are significant determinants of labor mobility. To examine and compare labor market dynamics in Argentina, Brazil and Mexico. Bosch and Maloney (2010) conducted a study using panel data from these three countries and Appling Markov transition processes. The main results indicate that there are broad commonalities among the three countries and more common patterns of worker mobility among sectors of inactivity and work. Tansel and Ozdemir (2015) study the determinants of transitions across formal/informal sectors in Egypt; this study provides Markov transition process probabilities differentiated by gender-based on worker flows across different employment states using Egypt Labor Market Panel Survey of 2012. The main results of this study indicate that government employment and the out of labor force are the least mobile labor market states. Informal private wage work and unemployment are the most mobile labor market states. Also, this study uses the multinomial logit regression to discuss the determinants of outflows from the labor market states. Only a few of the explanatory variables except the high levels of education are found to have predictive power in explaining the transitions from unemployment, formal wage, informal wage and self-employment labor market states. In a comparative study conducted by Assaad and Krafft (2016) to study “labor marker market dynamic and youth unemployment the middle east and north Africa: evidence from Egypt, Jordan and Tunisia”using data from Egypt, Jordan and Tunisia. “The three rounds of the Egypt Labor Market Panel Survey (ELMPS), fielded in 1998, 2006, and 2012, the Jordan Labor Market Panel Survey (JLMPS) of 2010, and the Tunisian Labor Market Panel Survey (TLMPS) of 2014. This study estimates a complementary log-log model with gamma frailty on annual data for the probability of ending an unemployment spell in each year. This model is also known as the Prentice-Gloeckler-Meyer model”(Meyer, 1990). The main results of this study are that the education of the unemployed in the three countries also very important contrasts. While 87% of the unemployed in Egypt in 2012 had secondary education or higher, that share falls to 54% in Jordan in 2010 and 44% in Tunisia in 2014. In Egypt, individuals with less than secondary education have no chance of obtaining formal jobs and therefore have little incentive to remain unemployed searching for such jobs. “In contrast an individual with a basic education in Jordan and Tunisia has nearly twice the probability of getting either a public sector job or a formal private sector job than a similar individual in Egypt, giving them more reasons to remain unemployed for some time while seeking these jobs.”In all three countries, there is a large number of educated young women who want to make use of their educational experiences but lack the mobility to move to where the jobs are. A study conducted by Assaad and Krafft (2017) investigates “how labor market insertion for Egyptian youth and how-to nature of youth transitions relate to gender and social class.” The main result of this study is that youth today face a poorer chance of transitions into a good job than previous generations. Also, social class is the main pillar in determining the success of the transition from school to work in Egypt. Assaad et al. (2018) examine female labor force participation over time for MENA countries –Algeria, Egypt, Jordan and Tunisia –control by marital status, educational attainment and place of residence (urban/rural) differences using multivariate logit models estimated on annual labor force survey data by country to simulate trends in female participation in different types of participation (public sector work, private wage work, private non-wage, unemployment and non-participation. The main results indicate that “the decline in the probability of public, sector employment for women with higher education is associated with either an increase in unemployment or a decline in participation.” Transition from unemployment to employment 19 This study proposes to fill the gap in the literature about the impact of different levels of education separately on remaining in unemployment and transition from unemployment to employment state using eight binary logistic regression models. 3. Data source The data used in this paper come from the 2018 wave of the Egypt Labor Market Panel Survey (ELMPS 2018). “The ELMPS 2018 is the fourth wave of a longitudinal survey carried out by the Economic Research Forum (ERF) in cooperation with the Egyptian Central Agency for Public Mobilization and Statistics (CAPMAS). The 2018 wave follows previous waves in 1998, 2006, and 2012. The panel design of the survey offers substantial advantages over pooled cross-sectional data by allowing for a more accurate assessment of change over time that controls for both observable and unobservable individual and household characteristics. It also allows for a unique perspective on life course transitions by allowing researchers to link life course outcomes like education, employment, marriage, etc.”(Assaad et al., 2019). “The first round of the panel was the ELMS 1998. It included a nationally representative sample of 4,816 households with 23,997 individuals. The second round ELMPS 2006 located 3,685 households from the ELMS 1998 the first round. An additional 2,168 new households were resulting from splits and a refresher sample of 2,498 households to ensure the representativeness of the sample. These add up to 8,351 households with 37,140 individuals. The third round of ELMPS 2012 includes a total of 12,060 households and 49,186 individuals. The attrition from the 2006 sample to the 2012 sample was not random and due to several processes. 12,060 households included 6,752 households located from the 2006 sample, 3,308 new households resulting from splits from these households, and a refresher sample of 2000 households. 28,770 individuals from the 2006 sample were successfully in 2012 forming a 2year panel. A total of 13,218 were also tracked in 1998 forming a panel covering three points in time”(Tansel and Ozdermir, 2015). “The fourth round of ELMPS 2018 included 15,746 households and 61,231 individuals. Of these households, 13,793 households included members from 2012 (10,042 panel and 3,751 split households) and 1,953 were refresher households. Among individuals, 53,040 were in households that included at least one individual interviewed in 2012 (i.e. either panel or split households), while 8,191 were in refresher households. Of the 49,186 individuals included in the 2012 sample, 39,153 (79.6%) were successfully re-interviewed in 2018. Of the 37,140 individuals in the 2006 sample, 22,901 (61.7%) were successfully tracked over three waves. Finally, of the 23,997 individuals included in the 1998 wave, 10,145 (42.3%) were successfully tracked over four waves”. For the specific aim of this paper, the panel sample extracted covers the labor force defined by those between the ages of 15–64 years who responded to employment status questions in both 2012 and 2018. That corresponds to 18,408 individuals (Assaad et al., 2019). 4. Methodology To achieve the objects of this study, two approaches are used: the transition probabilities matrix analysis and multivariate statistical analysis using binary logistic regression. 4.1 First approach: the transition probabilities matrix To constriction the transition probabilities matrix, we need to calculate Pij, where Pij “is the probability of finding a worker in stat jat the end of the period given that he/she was at state i at the beginning of the period.”For each cell of the transition probability matrix, the simple probability (the maximum likelihood estimator) is given by Pij ¼nij ni, where nij is the number of workers who were in state iand move to state jbetween the period tand tþ1 (in this study between 2012 and 2018); and niis the number of workers who were in state iin period t. In this REPS 10,1 20 study, we have three labor market states. They are as follows: employment state, unemployment state and OLF state. Employment state is including formal/informal private regular wage work, irregular wage work, government (includes government administration and public enterprises) and self-employed agriculture/non-agriculture. Unemployment is based on standard definition “including individuals who are not working but actively searching for a job.”Out of Labor Force (OLF) “includes individuals who are not working but not searching for a job.” 4.2 Second approach: the multivariate statistical analysis To determine the impact of the main factors, especially education levels affecting remaining in the unemployment state or transition from unemployment state to employment state, two groups of binary logistic regression models are used. The first group contains four models related to remaining in the unemployment state and the second group contains also four models related to the transition from unemployment state to employment state. To estimate the impact of different education levels: no education, less than intermediate, intermediate and above and higher education on remaining in unemployment state (dependent variable: 1 if the worker remains in unemployment state (between 2012 and 2018) and 0 otherwise). To estimate the impact of different education levels: no education, less than intermediate, intermediate and above and higher education on the transition from unemployment state to employment state (dependent variable: 1 if worker move from unemployment state to employment state (between 2012 and 2018) and 0 otherwise). This study fits eight regression models divided into two groups: (1) the models related to remaining in unemployment state (four models); (2) the models related to the transition from unemployment state to employment state (four models). The binary logistic regression model is adapted because of the dichotomous nature of the dependent variable. In a general form, binary logistic regression models with a set of socioeconomic factors that affect remaining in unemployment state and transition from unemployment to employment state are taking the following forms (Chong et al., 2008): ProbðY¼1jXt−1Þ¼FðβXt−1Þ where X’s are the various characteristics of workers, that determine their probabilities of moving between the different employment states (unemployment and employment). These factors include gender (represented by male takes the value of 1 and zero otherwise), age (15– 64) (represented by three categories: youth age group (15–24), middle-aged group (24–44) and elder-aged group (45–64)) (used the elder-aged group as a reference), education levels (represented by four categories: no education, less than intermediate, intermediate and above, and higher education) and it will introduce separately, marital status (takes the value of 1 if the worker is not married and zero otherwise) and place of residence (takes the value of 1 if the worker resided in an urban area and zero otherwise). 5. Statistical analysis 5.1 Descriptive statistics Three different labor market states are identified. They are as follows: employment state, unemployment state and OLF state. The share of each labor market state in the total labor force between 2012 and 2018 is shown in Figure 1. The proportions for the labor market status are quite similar in employment, unemployment, and the OLF state during 2006–2018. The trend of the labor force across labor market states is quite similar from 2006 to 2018. Figure 1 indicated that the share of employment state slightly increased from about 50% in 2006 to about 51% in 2018. The unemployment state remains stable around 5% (4.3% in 2006 vs 4.5% in 2018). Further, the decline in the OLF state is from about 47% in 2006 to about 44% in 2018. Transition from unemployment to employment 21 Table 1 gives the shares of each labor market state in the male and female samples separately. In the male sample, it is substantial increases in the employment state from about 75% in 2006 to about 82% in 2018, while it is a substantial decrease in the OLF state from about 21% in 2006 to 13.5 in 2018. The unemployment state remains stable at around 4%. In the female sample. The share of employment is about 21–23% through the period between 2006 and 2018. The largest labor market state is the OLF state with about 71–74% share between 2006 and 2018. The unemployment state remains stable at around 5%. 5.2 Transition probabilities matrix analysis In this section, we construct the transition probabilities matrix to identify the factors that may make more movements from unemployment to employment. These factors include gender, age, education level, marital status and place of residence respectively. 5.2.1 Transition probabilities matrix for the total sample. Table 2 shows the transition probabilities matrix for Egypt’s labor market between 2012 and 2018. The matrices show the proportions of individuals who stayed in the same state (employment, unemployment and out of labor force) or move from one to others, across the six years, “the main diagonal shows the %ages of individuals who remained in a given state”between 2012 and 2018. The results of Table 2 referents high levels of diagonal elements in the whole sample, employed individuals in 2012 remain in the same original state in 2018 with 83.5% and OLF state is the second less mobile state with 75% remaining in this state between 2012 and 2018. While unemployment state shows high mobility with only about 12% remaining in the state between 2012 and 2018. Employed individuals are the least-mobile among all other labor market groups. The transition probability of flows from employment state to unemployment state is very 0 10 20 30 40 50 60 2006 2012 2018 Employment Unemployment Out of Labor Force Source(s): ELMPS 2018 2006 2012 2018 Male Employment 74.75 86.55 82.3 Unemployment 3.84 3.57 4.2 OLF 21.41 9.88 13.5 Female Employment 23.71 22.08 21.2 Unemployment 4.93 6.45 4.8 OLF 71.36 71.47 74.0 Source(s): Computed by the research using ELMPS 2018 Figure 1. Trends of labor market states in the total sample (%), 2006– 2018, Egypt Table 1. Trends of labor market states in the male and female samples, 2006– 2018, Egypt REPS 10,1 22 little with 3%. The probability of transition from the employment state to the OLF state is 13.7%. The unemployed individuals display huge movement among all other labor market groups. The transition probability of flows from the unemployment state in 2012 to the employment state in 2018 is about 33%, while the transition probability of flows from the unemployment state in 2012 to the OLF state in 2018 is about 55%. OLF state is the second low mobile after employment. The transition probability of flows from the OLF state in 2012 to the employment state in 2018 is about 19%. Only about 6% of OLF state in 2012 move into the unemployment state in 2018. 5.2.2 Transition probabilities matrix analysis for gender. Table 3 shows the transition probabilities matrix for gender between 2012 and 2018. The results of Table 3 indicate that the probability of remaining in employment state from 2012 to 2018 is somewhat similar in both the matrix of male and the total sample (88.9% vs 83.5%), while is much lower for female than the total sample (62.2% vs 83.5%). The proportion of workers who remain in the employment state from 2012 to 2018 is the highest probability in the male sample (88.9%), while in the female sample, the proportion of remaining in OLF state is the highest probability with 83.6%. It means that the OLF state is dominated by females. In the male sample, the probability of remaining in the unemployment state from 2012 to 2018 is much lower than in the total sample (about 9% vs about 12%), while in the female sample the probability is quite similar to the total sample (about 13% vs about 12%). The transition probability of flows from employment state to unemployment state is very little in both the male and female samples with 3.1 and 1.1% respectively. In the male sample 8% of males employed in 2012 move to OLF state in 2018, while 36.7% in the female sample in 2012 move to OLF state in 2018. It means that women prefer to move to the OLF state. 2018 2012 Employment Unemployment OLF Total Employment 83.5 2.8 13.7 100 Unemployment 32.9 12.2 54.9 100 OLF 19.3 5.7 75.0 100 Total 51.3 4.5 44.2 Source(s): Computed by the research using ELMPS 2018 2018 2012 Employment Unemployment OLF Total Male Employment 88.9 3.1 8.0 100 Unemployment 79.1 9.4 11.5 100 OLF 54.5 8.5 37.0 100 Total 82.5 4.2 13.2 Female Employment 62.2 1.1 36.7 100 Unemployment 14.2 13.3 72.5 100 OLF 11.4 5.0 83.6 100 Total 21.3 4.7 74.0 Source(s): Computed by the research using ELMPS 2018 Table 2. Transition probabilities matrix for the total sample, 2012–2018, Egypt Table 3. Transition probabilities matrix for gender, 2012– 2018, Egypt Transition from unemployment to employment 23 As a whole, the results of the logistic regression models consistent with the transition probabilities matrix results, except for a few cases. Based on the above findings, there is enough evidence to accept the null hypothesis that no education has a positive significant impact to transition unemployed individuals from unemployment to employment, while less than intermediate as well as higher education have a negative significant impact to transition unemployed individuals from unemployment to employment. 6. Conclusion and recommendations 6.1 Conclusion The problem of unemployment is a global problem that affects the world’s economies and sometimes it is called the body of the economy. Unemployment is an important and pivotal problem, regardless of time, in the past, and also in the present. In Egypt, the unemployment rate has slightly decreased from 12.7% in 2012 (9.3% for males vs 24.1% for females) to about 11.8 in 2017 (8.2% for males vs 23.1% for females), the unemployment rate is high for females compared with males over 2012–2017, while it is slightly decreased from 24.1% to 23.1% during this period. The unemployment rate is high especially among the highly educated, about 33.2% in 2012 and about 34% in 2017, while it is very low among illiterates with about 3.2% in 2012 and 3% in 2018 (CAPMAS, 2019). It is clear that unemployment among highly educated is much higher compared with illiterates and it slightly increased during this period, while it is very low among illiterates and slightly decreased during the same period. Due to this issue, it is useful to address this problem and try to understand the impact of different levels of education separately on the transition from unemployment to employment, to help the policymakers to eradicate the gap between education and the demand of the labor market in Egypt. In this study, the transition probabilities matrix is differentiated by gender, age groups, educational levels, marital status, and place of residence based on worker flows across employment, unemployment and out of labor force states during the period 2012–2018 using Egypt Labor Market Panel Survey of 2018. Also, this study examines the impact of different educational levels separately on remaining in unemployment and on the transition from unemployment to employment using eight binary logistic regression models. In the whole sample, the results indicate that employed individuals are the least-mobile among all other labor market groups. Unemployed individuals display huge movement among all other labor market groups. The transition probability of flows from employment state to unemployment state is very little in both the male and female samples. In the male sample, unemployed individuals are highly movement among all other labor market groups with about 79%, while only about 14% in the female sample in 2012 move from unemployment state to employment state in 2018. The transition probability of flows from the employment state is very little in the elder-aged group, followed by the middle-aged group and youth age group. In the elder-aged group, unemployed workers are highly movement to employment state among all other labor market groups, followed by youth age group and middle-aged group respectively. The workers with high education are the least mobility from employment state followed by workers with intermediate and above, less than intermediate, and no education respectively. Also, the results indicate that the workers with no education make the most transition from unemployment state to employment state-this transition is a highly important result-, followed by workers with less than intermediate, workers with higher education, and workers with intermediate and above respectively. The workers who are married are the least mobility from employment state compared with the workers who are mot married. Also, the results indicate that the workers who are not married make a greater transition from the unemployment state to the employment state compare with the workers who are married. REPS 10,1 30 The probability of remaining in the employment state from 2012 to 2018 is quite similar for workers who resided in urban or rural areas. Workers residing in urban areas are more mobile from the unemployment state in 2012 to employed state in 2018 than those residing in rural areas. About 39% of workers residing in urban areas are move from unemployment state in 2012, while about 27% of those resident rural areas make a similar transition. The study concluded that by using the transition probabilities matrices it can link workers’movement between 2012 and 2018 and their characteristics to identify the factors that made more movements from unemployment to employment that the main results of transitions from unemployment to employment are relatively large for males, elder-age, uneducated workers as well as workers who are not married and urban residents. To estimate the impact of different education levels: no education, less than intermediate, intermediate and above, and higher education on the transition from unemployment state to employment state (dependent variable: 1 if worker move from unemployment state to employment state (between 2012 and 2018) and 0 otherwise). This study fits eight regression models divided into two groups: (1) the models related to remaining in unemployment state (four models); (2) the models related to the transition from unemployment state to employment state (four models). The binary logistic regression model is adapted because of the dichotomous nature of the dependent variable. The main results of the binary logistic regression models indicated that education variables seem to play an important role in the transition from unemployment to employment, unemployed individuals with no education are more likely to move from unemployment to employment compared to the others, while unemployed individuals with less than intermediate and with higher education are less likely to move from unemployment to employed compared to the others (intermediate and above is insignificant). Also, the results show that unemployed individuals ages 15–24 years (young age group) are significantly more likely to move from unemployment to employment compared to their elder-aged group (45–64) as well as unemployed individuals ages 25–44 years (middle-aged group) are significantly more likely to move from unemployment to employment compared to their elder-aged group (45–64). Besides, unemployed individuals who are not married are significantly more likely to move from unemployment to employment compared to their married individuals. Unemployed individuals who reside in urban areas are significantly more likely to move from unemployment to employment relative to those who reside in rural areas. As a whole, the results of the logistic regression models consistent with the transition probabilities matrix results, except for a few cases. Based on the above findings, there is enough evidence to accept the null hypothesis that no education has a positive significant impact to transition unemployed individuals from unemployment to employment, while less than intermediate as well as higher education have a negative significant impact to transition unemployed individuals from unemployment to employment. 6.2 Recommendations Based on the main findings derived from this study about the impact of different education levels especially higher education and no education levels on the transition from unemployment to employment, the following are important recommendations may help the policymakers to eradicate the gap between education and the demand of the labor market in Egypt. (1) Skill mismatching. The education system needs to start determining the skills that youth are needed to find rewarding work. Education institutions, especially higher education institutions should work to strengthen the personals skills of the graduates. Transition from unemployment to employment 31 (2) Education and training program. The high prevalence of unemployment among educated, especially among university students. Higher education graduates’ training strategy must be developed so that new graduates who are awaiting job appointments can have a working environment experience. (3) Investment in and promote vocational training for the youth with intermediate and above or technical education. (4) Education institutions need to develop and innovate their program curricula and inclusive teaching and learning materials to be able to meet global standards for monitoring the quality of the program to cope with the labor market. (5) Encourage entrepreneurship among youth through facilitating financial support as providing loans with small interest (Barsoum et al., 2014). 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(2014), “Labour mobility and the informal sector in algeria: a cross-sectional comparison (2007–2012)”, Institute for Labor Studies and Public Policies (TEPP) Working Paper, No: 2014-7, University Paris-Est Cr eteil. Boughzala, M. and Kouki, M. (2003), “Unemployment persistence and the informal sector”, Economic Research Forum (ERF), Working Paper, No. 200326. Mroz, T. and Savage, T. (2006), “The long-term effects of youth unemployment”,Journal of Human Resources, Vol. 41 No. 2, pp. 259-293. Corresponding author Amany Yashoa Gad can be contacted at: [email protected]du.eg;[email protected] For instructions on how to order reprints of this article, please visit our website: www.emeraldgrouppublishing.com/licensing/reprints.htm Or contact us for further details: [email protected] Transition from unemployment to employment 33