metadata: authors: 0: author: Ferhat,Samia
1: author: Joubert,Clement Jean Edouard
count: Egypt, Arab Republic of
docna: 0: docna: The Early Career Dynamics of Informality and
            Underemployment : Evidence from the Arab Republic of Egypt
docty: Policy Research Working Paper
abstracts: cdata!: This paper studies the early career
            dynamics of employment and formality using data from the
            Arab Republic of Egypt. With 14 rounds of Egypt&#x2019;s
            labor force surveys several measures of informality and
            underemployment are constructed to examine how the labor
            market conditions faced by young men when they exit school
            shape their future employment trajectories. Employment
            outcomes at different levels of potential experience are
            linked to cross-cohort, cross-regional and cross-schooling
            level variation in labor market conditions at graduation to
            achieve identification. The results show that cohorts of
            Egyptian men who enter a labor market in which employment
            rates are high (low) are only better (worse) off for a few
            years. These fast mean reversals stand in contrast to the
            typical finding from rich countries that scarring effects
            persist through the first decade of a worker&#x2019;s career.
display_title: The Early Career Dynamics of
            Informality and Underemployment : Evidence from the Arab
            Republic of Egypt
listing_relative_url: /research/2023/06/34102069/early-career-dynamics-informality-underemployment-evidence-arab-republic-egypt
topicv3: Education m1327048 470,Social Protection and Labor m1327194 697,Jobs m1503931 2670
publishtoextweb_dt: 2023-06-26T00:00:00Z
dois: 10.1596/1813-9450-10499
origu: Off of Sr VP Dev Econ/Chief Econ (DECVP)
author_keys: 0: author_key: 2161180
1: author_key: 1411340
model_extractions: Policy Research Working Paper 10499 The Early Career Dynamics of Informality and Underemployment Evidence from the Arab Republic of Egypt _Samia Ferhat Clément Joubert_ Development Economics Development Research Group June 2023
Policy Research Working Paper 10499

# **Abstract**

This paper studies the early career dynamics of employment and formality using data from the Arab Republic of Egypt. With 14 rounds of Egypt’s labor force surveys several measures of informality and underemployment are constructed to examine how the labor market conditions faced by young men when they exit school shape their future employment trajectories. Employment outcomes at different levels of potential experience are linked to cross-cohort, cross-regional and cross-schooling level variation in labor market conditions at graduation to achieve identification. The results show that cohorts of Egyptian men who enter a labor market in which employment rates are high (low) are only better (worse) off for a few years. These fast mean reversals stand in contrast to the typical finding from rich countries that scarring effects persist through the first decade of a worker’s career.

This paper is a product of the Development Research Group, Development Economics. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/prwp. The authors may be contacted at cjoubert@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
# The Early Career Dynamics of Informality and Underemployment:

Evidence from the Arab Rebuplic of Egypt<sup>*</sup> Samia Ferhat<sup>†</sup> Cl ́ement Joubert<sup>‡</sup>

## **JEL Classification: F51, E24, E26, Z12**

**Keywords:** Informality, scarring effects

> *We are grateful to the Economic Research Forum for providing access to the data, and Kathleen Beegle, John Giles, Nishta Sinha, Paolo Verme, Nelly Ellmallakh, Daniel Lederman, and S Anukriti for helpful discussions and comments. The authors gratefully acknowledge funding from the MENA Chief Economist unit at the World Bank and the World Bank’s research group. The findings, interpretations, and conclusions expressed in this work do not necessarily reflect the views of the World Bank, its Board of Executive Directors, or the governments they represent. The World Bank does not guarantee the accuracy of the data included in this work

> †DARES. E-mail: samia.ferhat@cyu.fr

> ‡World Bank. E-mail: cjoubert@worldbank.org (corresponding author).
# **1 Introduction**

Informality and under-employment are pervasive and associated with stagnant productivity in low and middle income countries (Busso et al. (2012)). Informality rates have not declined significantly in most countries despite a general trend towards reducing payroll tax wedges in formal employment, and even in the presence of high economic growth (La Porta and Shleifer (2014)). One reason for persistent informality is that the formal private sector has struggled to absorb bulging cohorts of youth exiting school, a problem that is likely to persist given that the median population age is lower than labor market entry in many cases.

This paper studies the extent to which the labor market opportunities faced by young men entering the labor market shape their access to formal employment and broader employment trajectories. It uses data from 14 rounds of the Arab Republic of Egypt’s labor force survey that record employment outcomes for individuals in successive graduation cohorts. The dataset contains information on multiple measures and proxies for formal vs. informal employment and under-employment, such as working without social security coverage, being employed full-time, working without a contract, working for a wage, working for the public sector, working in agriculture, and the size of the employing establishment (number of employees). We exploit variation across cohorts, regions, and schooling levels in the labor market conditions faced by an individual upon graduating to achieve identification, following Schwandt and von Wachter (2019).

Middle-Eastern and North African countries (“MENA region”), and Egypt in particular, exemplify the “youth bulge” phenomenon.<sup>1</sup> Public employment historically absorbed labor market slack, creating distortions in the supply of skills by encouraging credentialism and overeducation.<sup>2</sup> Youth labor markets are thus characterized by high levels of informality, unemployment, or underemployment while queuing for public jobs (Assaad and Krafft 2015). In this context, the opportunities of the successive cohorts entering the labor market can vary substantially depending on shocks affecting private but also pub-

> 1See Assaad and Roudi-Fahimi (2007), Saliola (2022), and projections by Assaad (2022)

> 2El-Kogali and Krafft (2019)

1
lic labor demand as governments’ fiscal spaces are often directly connected to commodity prices through price subsidies (Ghoneim 2015).

Studies on the effect of graduating in a recession in developed countries (Kahn (2010a), Oreopoulos et al. (2012), Altonji et al. (2016)) suggest that labor market conditions immediately following graduation can have long-lasting effects on the future earnings and employment prospects of young workers. These studies exploit fluctuations in the labor market conditions faced at labor market entry by successive cohorts of graduates for identification. This paper draws on this identification strategy to pin down the impact of shocks to labor market opportunities at the time of labor market entry on the subsequent outcome trajectories of young Egyptian males. It contributes to the understanding of career dynamics and life trajectories in the MENA region: the literature has documented declining probabilities of transitioning from school to “good” jobs (Assaad et al. (2019), Assaad and Krafft (2021)) and analyzed the determinant of unemployment dynamics using survival analysis models (Assaad et al. (2016)), but the causal effect of initial labor market conditions of subsequent employment and life outcomes in the region has not been measured.

We also contribute to the broader literature on informality dynamics. Cross-sectional characteristics of informal employment are well-documented: it is associated with low wages, low-productivity, smaller establishments, self-employment, and lack of social protection A strand of the literature considers how infor- coverage. macro-dynamics, e.g. mality co-moves with the business cycle (Loayza and Rigolini (2006), Bosch and Maloney (2008)) but the micro-dynamics of informality are the object of much less research. Existing studies have described short-term transitions in informality status and informal wages (e.g. Bosch and Maloney (2010), Pratap and Quintin (2006) Bargain and Kwenda (2010)). Few papers focus on career or life-cycle dynamics beyond basic descriptions (see Gatti et al. (2014), for MENA countries including Egypt) even though they may determine the investment and accumulation of skills, or the design of social protection programs (Joubert (2015)).

Life cycle dynamics can also shed light on the nature of informality. A simple model in which individuals randomly search for a rationed number of highly-valued formal 2
jobs would predict that rates of formality increase with age as individuals repeatedly attempt to access good jobs and never leave them. In such a model, the age-formality gradient reflects the arrival rate of formal jobs, i.e. the degree of rationing. In contrast, other theories of informal that or non- employment emphasize comparative advantage pecuniary benefits do not structurally produce life cycle dynamics (Clark et al. (2017)).

We find that cohorts of Egyptian men who exit school when labor market conditions are bad do not exhibit the same persistent scarring effects identified in rich countries (e.g. Kahn (2010b), Oreopoulos et al. (2012)). Instead, they are worse off for at most a few years and only in terms of the quantity but not the quality of employment. More puzzling, good initial conditions in fact are associated with negative outcomes after 5 or 6 years of potential experience. For example, those who faced a better labor market when they exited school (as measured by high employment rates) are less likely to be employed, to have social security coverage, to have a full time job, to have a contract, and to be wage workers, and they work fewer hours, especially towards the end of their first decade of labor market experience.

We that these results that variation in labor market con- hypothesize reflect cyclical ditions dominates effects. Cohorts that enter into a bad labor market end scarring up profiting from eventual economic recoveries, and are not strongly penalized by having a bad start. The negative correlation between initial employment rates and eventual employment quality could also be consistent with a model of queuing in which some cohorts forgo initial employment opportunities in order to secure rationed quality jobs down the road (and vice versa). We concluded that data sets spanning longer periods are needed to confirm either hypothesis.

# **2 Data**

## **2.1 Measures of informality**

Three measures of informal employment are most often used in the literature, see Wahba and Assaad (2017). One is the type of contract (officially written or not), another is 3
whether the individual has social security coverage or not, and the last one is the size of the employing firm or establishment (firms with fewer than 5 employees are often assumed to be informal). In addition to these measures of informality, we also consider other aspects of employment quality to describe employment trajectories post graduation such as: full wages, time employment, whether the individual is a wage worker or not, employed in the public sector, or employed in the agriculture sector. To complete the picture, we also consider the quantity of employment, namely whether an individual is employed and the number of hours worked per week.

We use Labor Force Surveys (LFS) between 2006 and 2019 to construct the employment outcomes.<sup>3</sup> Labor Force Surveys were first conducted in Egypt in 1957 by the Central Agency for Public Mobilization and Statistics ”CAPMAS”, on a quarterly, biannual, and annual basis. In 2006, CAPMAS improved the questionnaire by re-ordering existing questions and creating new ones to better capture labor market conditions.<sup>4</sup> The Economic Research Forum (ERF) cleaned and harmonized the data as a part of a project that started in 2009, in which major efforts were devoted to facilitate micro data access and use in Arab countries (OAMDIA (2018b)).

In our main model we use a labor market indicator (the regional rate of employment) that is constructed directly from the 2006 through 2019 LFS survey rounds. Therefore we must restrict the estimation sample to individuals who exited the schooling system during those years. The indicators used in our second model (youth employment rate, GDP growth, oil and wheat prices) are only available at the national level but cover 1990 onwards. In that case we can include all individuals who graduated after 1990. Besides 3Data were obtained from the Economic Research Forum: OAMDIA (2019a), OAMDIA (2019b), OAMDIA (2019c), OAMDIA (2018a), OAMDIA (2018b), OAMDIA (2016a), OAMDIA (2016b), OAMDIA (2016c), OAMDIA (2017), OAMDIA (2018c), OAMDIA (2018d), OAMDIA (2019d), OAMDIA (2021a), OAMDIA (2021b).

> 4In 2008, the representativity of the sample was improved at the governorate level. This implies that indicators constructed at the governorate level may contain measurement error in the 2006 and 2007 rounds. We check the robustness of our results to excluding these two rounds.

4
restricting the sample to cohorts defined by the year of exiting the schooling system, we also restrict it to males because we want to abstract from the broader set of economic mechanisms that must be invoked to explain female employment trajectories.

Table 1 provides summary statistics of the employment outcomes analyzed for the sample of males 15 years of age and older, who left the schooling system between 2006 and 2019, and who are not currently enrolled in the schooling system. The total number of individuals observed in the pooled survey rounds is 263,743, out of which 68% are employed. Table 1 also shows that only 25% of employed individuals in the sample are employed with a contract (written and/or official), 58% are employed full time. 31.6% of working individuals are wage workers and the agriculture sector employs 25% of the sample.

# **2.2 Early career dynamics of informality and underemployment**

Our analysis of informality and under-employment organizes the data by graduation cohort defined by the year of exit from the schooling system. We also use the term labor market entry or labor force entry, abstracting for simplicity from the possibility that young men may exit school and not look for work. The LFS do not record an individual’s year of graduation, therefore we impute it based on the typical age of graduation for the schooling level attained by each individual and their age at the time of the survey. Specifically, we use 22 as the graduation age of a tertiary- educated individual, 18 for a secondary educated individual, and 12 for a primary educated individual. As discussed in section 3, while this procedure implies some loss of efficiency, it is usually applied in the literature (e.g. Kahn (2010a)), even when actual graduation ages are available, to remove a source of endogeneity whereby individuals time their entry in the labor market based on labor market conditions. We construct potential experience as the difference between the age at the time of the survey and the imputed age at the time of graduation.

Figure 1 plots the evolution of employment outcomes describing young men’s situations in the labor market and access to higher quality jobs. Those improve overall as the time passes after graduation. Access to formal employment increases, as captured by the share 5
Table 1: Summary statistics for employment outcomes

||Mean/Sd|N|
|---|---|---|
|Employed|0.684<br>(0.465)|263743|
|Employed with social<br>security coverage|0.199<br>(0.399)|178424|
|Employed full time|0.580<br>(0.494)|180113|
|Employed with a contract|0.250<br>(0.433)|119315|
|Employed in public sector|0.077<br>(0.266)|180113|
|Monthly wage|1600.044<br>(1643.353)|78219|
|Hours worked per week|31.173<br>(24.138)|263309|
|Wage worker|0.316<br>(0.465)|263743|
|Number of workers in establishment<br>of the main job|460.189<br>(385.523)|70729|
|Working in agriculture sector|0.250<br>(0.433)|179578|

Notes: (i) Variables are constructed based on Egyptian Labor Force Surveys 2006-2019. The sample of interest is males who graduated between 2006-2019, who are at least 15 years old and who have less than 15 years of potential experience. (ii) 31.2% of employed males in the sample did not state whether they are legally employed or working without a contract.

6
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(34.76%) average unemployment rate for males aged between 15-24. Figure 2 shows the evolution of these economic condition indicators over time. We can observe two main economic recessions, the first around 1999/2002 caused mainly by the Isareli-Palestinian conflict that heavily impacted tourism, an important sector in the Egyptian economy. The second economic recession occurred between 2008-2012, as a result of the succession of the financial crisis and the Arab Spring uprising against public figures causing instability in the country.

The unemployment rate is the classic labor market indicator. However, as noted in the literature, the unemployment rate is not a sufficient indicator of the economic situation at the time of graduation in the case of MENA countries and Egypt in particular (see: Krafft and Assaad (2015) and Assaad 2019). Better-off individuals can afford to remain unemployed rather than accepting a job that does not match their level of skills, a phenomenon known as “queuing”. Therefore, we estimate our model using the overall employment rate, which includes unemployed individuals. We also produced results (available upon request) using other indicators of labor market opportunities, as suggested by Assaad (2019): hours worked, the share of workers with social security coverage, the share of workers with a contract (officially written or not), the share of workers with full time contracts.

To compute each regional labor market conditions indicator, we consider individuals who are out of the schooling system aged 15 years of age and older. The indicator is obtained by averaging the corresponding individual measurement among observations in the same year, governorate of residence and education category. Since we do not have information on the governorate of residence at the time of graduation, we impute it using the governorate of residence at the time of the interview. Our data span 14 years, there are 29 governorates, and we construct four schooling categories, for a total of 1,475 combinations (excluding cells with fewer than 50 observations). Table 2 describes the variation in each labor market conditions indicator across the 1,475 data cells we have constructed.

8
Table 2: Summary statistics : labor market conditions at the governorate level

||Mean/Sd|N|p25|p50|p75|
|---|---|---|---|---|---|
|Employed|0.643<br>(0.192)|1470|0.548|0.671|0.774|
|Employed with social security coverage|0.162<br>(0.210)|1440|0.000|0.060|0.267|
|Employed full time|0.553<br>(0.208)|1440|0.426|0.568|0.695|
|Employed with a contract|0.195<br>(0.236)|1422|0.009|0.083|0.333|
|Employed in public sector|0.074<br>(0.124)|1440|0.000|0.009|0.115|
|Monthly wage|1626.489<br>(586.129)|1377|1279.949|1584.927|1875.000|
|Hours worked per week|29.144<br>(9.352)|1470|24.386|30.143|35.000|
|wage worker|0.283<br>(0.176)|1470|0.146|0.265|0.407|
|Number of workers in establishment<br>of the main job|398.810<br>(177.858)|1305|275.022|393.637|509.667|
|Working in agriculture sector|0.261<br>(0.224)|1440|0.071|0.222|0.423|

Notes: The variables are constructed based on the Egyptian Labor Force Survey waves between 2006-2019. Individuals in the sample are those who graduated between 2006-2019 and are aged 15 years of age and older. Data are collapsed on the cohort of graduation, governorate of residence and educational attainment.

Table 3: Summary statistics: economic condition indicators at the national level

||Mean/Sd|N|
|---|---|---|
|Youth employment rate, males<br>aged between 15-24|34.764<br>(4.322)|29|
|Oil price (US $ per barrel)|48.362<br>(31.787)|29|
|GDP growth|4.381<br>(1.598)|29|
|Wheat price (Us $ per bushel)|4.529<br>(1.549)|29|

Notes: The table provides the mean for each of the 4 variables on the country level (Egypt) over 29 years (1991-2019). Data source: World Bank data.

9
<!-- Start of picture text --> ® ey<br>2=<br>a<br>rt 3Do<br>£o =i"<br>=2<br>%<br>o e<br>aS) &ies<br>Ee 82<br>a! ha<br>pm<br>SN 88<br>° ci<br>D £<br>[os red)<br>o)xo} 3<br>= Re<br>1990 1994 1998 2002 2006 2010 2014 2018<br>Cohort<br>—®— Log wheat price —*— Log oil price<br>—e®— Gdp growth —®*®— Youth employment rate<br><!-- End of picture text -->
employed in a full time contract), _LMCscr_ 0 is a measure of labor market conditions faced by a given cohort and schooling level in a given region when entering the labor market, and calendar time _t_ is a deterministic function of cohort _c_ and potential experience _e_ . The main coefficient, _βe_ is allowed to vary by potential experience, to measure how the whole early career trajectory is affected by initial labor market conditions.

# **3.1 Identification and interpretation**

The inclusion of regions, educational attainments, year, and potential experience fixed effects implies that we are measuring deviations from the average career path, net of average differences across regions and schooling levels, and net of average annual variation. Therefore, we interpret the main remaining variation in _LMCscr_ 0 as arising from regional shocks to labor demand away from the country’s average situation. The identifying assumption is that these shocks are uncorrelated with the characteristics of the corresponding cohorts graduating in each region.

The literature has identified two main threats to identification for this empirical strategy.<sup>5</sup> First, schooling decisions (and thus the timing of labor market entry) can respond to labor market conditions: studies have shown that college graduates delay graduation when the labor market is bad. Those who decide to enter the labor market in a crisis could be different than those who delay their entry, generating selection bias. To deal with this issue, predicted labor market entry based on age and schooling level will be used instead of actual labor market entry as an alternative specification. Second, some of the measures of labor market conditions we use as independent variable (e.g. employment and unemployment rates) are aggregates of the labor supply decisions of young workers upon graduation. This creates a mechanical correlation between employment outcomes and our measure of labor market conditions at 0 potential experience. In those cases, the subsequent career profile (potential experience _gt_ 0) is still interpretable as capturing the persistence of initial employment conditions on future career outcomes.

Notice also that we do not include the current labor market conditions in the regres-

> 5See a discussion in Oreopoulos et al. (2012).

11
sions (only labor market conditions at the time an individual graduated); therefore, the regression coefficients should be interpreted as the effect of graduating in a recession, say, on the employment trajectory _given how labor market conditions tend to evolve after recessions_ (rather than the pure effect of one year of recession at graduation followed by normal conditions) (Oreopoulos et al. (2012)).

We first use as our independent variable the aggregate employment rates that vary at the governorate x year level (“regional model”). Because this measure is calculated using LFS data spanning 2006-2019, it is only available for cohorts that graduated in those years. To complement these results we consider other outcomes that are available from 1990 onwards but vary only by year (“national model”). In the national model, the identification exploits differences in the career profiles exhibited by each subsequent cohort. It cannot be ruled out that a spurious correlation arises where differences between cohorts in, say, social norms, happen to be correlated with the evolution of labor market conditions.

# **4 Results**

We present results from estimating equation 1 on the sample described in section 2 in tables 4 through 9 and appendix tables A1 through A6. The tables report the coefficients that multiply the interaction between measures of labor market condition at labor market entry and years of potential experience. Coefficients on the other control variables such as the sets of dummies, are available from the authors upon request. Our main set of results exploit regional variation in employment rates (“regional model”) between 2006 and 2019. We assess their robustness by considering a broader set of measures of macroeconomic conditions over a longer time period (1990-2019) that vary only at the national level (“national model”).

## **4.1 Regional model**

Tables 4 and 5 present results using regional employment rates as our independent variable. The individual employment outcomes of interest are aggregated by cells defined by 12
region, schooling level, year of graduation and years of potential experience. The model includes dummies for region, schooling, year, and potential experience, as well as a linear trend in the graduation year. Three types of outcomes are analyzed: (i) measures of the quantity of labor supplied: fraction of individuals employed in each cell and average number of hours worked (unconditional on being employed), (ii) proxies for informality and under-employment (conditional on being employed): fraction of workers with social security coverage, fraction employed full-time, fraction with a contract, fraction working for a wage, fraction working for the public sector, fraction working in agriculture, and the size of the employing establishment (number of employees), and (iii) the logarithm of monthly wages (conditional on working for a wage).

Table 4: Results with regional labor market conditions indicators as control variables

|Dependent Variable|Hours Worked<br>for employed|Number of work-<br>ers in the establish-<br>ment|Ln(Monthly<br>wage)|Employment|Employed<br>in<br>public<br>sector|
|---|---|---|---|---|---|
|0b.pot<br>~~e~~xp#c.indepvar|29.26**|-87.12|0.215|0.817***|-0.0852|
|1.pot<br>~~e~~xp#c.indepvar|28.96***|80.11|0.117|0.607***|0.00553|
|2.pot<br>~~e~~xp#c.indepvar|12.50**|-187.5*|-0.0837|0.286**|-0.0482|
|3.pot<br>~~e~~xp#c.indepvar|7.792|10.02|0.262***|0.180*|-0.0616|
|4.pot<br>~~e~~xp#c.indepvar|-2.930|34.36|0.0410|-0.0531|-0.0148|
|5.pot<br>~~e~~xp#c.indepvar|-1.970|14.92|0.0669|-0.0261|-0.0969**|
|6.pot<br>~~e~~xp#c.indepvar|-10.62**|55.66|-0.0138|-0.233***|-0.102**|
|7.pot<br>~~e~~xp#c.indepvar|-22.14**|5.145|-0.0226|-0.415**|-0.0608|
|8.pot<br>~~e~~xp#c.indepvar|-18.09***|166.1|-0.0439|-0.326***|-0.0530|
|9.pot<br>~~e~~xp#c.indepvar|-19.38***|181.2|-0.0839|-0.352***|-0.0282|
|10.pot<br>~~e~~xp#c.indepvar|-10.01*|596.0**|-0.104|-0.204**|0.165|
|# obs|8,986|7,338|7,449|8,986|8,73|
|R-squared|0.515|0.293|0.828|0.360|0.602|
|Independent Variable|employment|employment|employment|employment|employment|

Notes: (i) Dependent variables on the individual level in each column are regressed on employment rate for individuals in the sample collapsed by governorate of residence and graduation cohort, interacted with potential experience from year 0 to 10. The potential experience is captured by the difference between the individuals age at the year of the survey and their age at the year of graduation. (ii) Year, cohort , governorate and potential experience fixed effects are included in each regression. Controls for eudcational attainment are also included.

(iii) Sample of males who graduated between 1990-2019. Data are constructed from LFS 2006-2019.

(iv) Cluster-robust standard errors in parentheses (clustering on cohort and governorate of residence).

(v) * , ** and ***refer to significance at the 10, 5 and 1% levels respectively.

The results tables show the coefficient estimates associated with the interaction between initial labor market conditions and years of potential experience from 0 to 10.<sup>6</sup> The

> 6Potential experience is top coded at 10. Only individuals with 15 years of potential labor market

13
Table 5: Results with regional labor market conditions indicators as control variables (ctd)

|Dependent Variable|Employed with<br>social<br>security<br>coverage|Employed<br>full time|Employed<br>with a contract|Wage Worker|Employed in<br>Agricultural<br>sector|
|---|---|---|---|---|---|
|0b.pot<br>~~e~~xp#c.indepvar|0.0769|0.322*|0.0220|0.490**|0.438***|
|1.pot<br>~~e~~xp#c.indepvar|0.0522|0.298**|-0.113|0.340***|0.189*|
|2.pot<br>~~e~~xp#c.indepvar|-0.0514|0.143|-0.0486|0.112|0.175*|
|3.pot<br>~~e~~xp#c.indepvar|-0.0904**|0.162|-0.0961|0.0212|0.0962|
|4.pot<br>~~e~~xp#c.indepvar|-0.154*|-0.0550|-0.150**|-0.175**|0.0228|
|5.pot<br>~~e~~xp#c.indepvar|-0.217***|-0.130|-0.207***|-0.233***|0.0174|
|6.pot<br>~~e~~xp#c.indepvar|-0.239***|-0.221**|-0.196**|-0.288***|-0.0842|
|7.pot<br>~~e~~xp#c.indepvar|-0.225***|-0.311***|-0.187**|-0.456***|-0.166*|
|8.pot<br>~~e~~xp#c.indepvar|-0.302***|-0.473**|-0.191***|-0.537***|-0.107|
|9.pot<br>~~e~~xp#c.indepvar|-0.175**|-0.427**|-0.0305|-0.467***|-0.132*|
|10.pot<br>~~e~~xp#c.indepvar|0.00421|-0.267*|0.126|0.161|-0.187|
|# obs|8,644|8,730|8,078|8,986|8,728|
|R-squared|0.776|0.474|0.758|0.647|0.687|
|Independent Variable|employment|employment|employment|employment|employment|

Notes: (i) Dependent variables on the individual level in each column are regressed on employment rate for individuals in the sample collapsed by governorate of residence and graduation cohort, interacted with potential experience from year 0 to 10. The potential experience is captured by the difference between the individuals age at the year of the survey and their age at the year of graduation.

(ii) Year, cohort , governorate and potential experience fixed effects are included in each regression. Controls for eudcational attainment are also included.

(iii) Sample of males who graduated between 1990-2019. Data are constructed from LFS 2006-2019.

(iv) Cluster-robust standard errors in parentheses (clustering on cohort and governorate of residence).

(v) * , ** and ***refer to significance at the 10, 5 and 1% levels respectively.

14
coefficients on the first line (interaction with 0 years of experience) describe how each employment outcome is affected contemporaneously by labor market conditions during the first year after labor market entry. The rest of the coefficients describe how the subsequent career profile is affected by initial labor market conditions. The effect of increasing by 1 percentage points the initial employment rate faced by a new entrant is obtained by dividing the corresponding coefficient by 100.

We first look at the quantity of labor supplied, measured by the employment rate and the unconditional number of hours worked. The first coefficient at the top of the column “Employment” in table 4 shows the correlation between the overall employment rate in a given year and the employment rate achieved by new entrants in the labor market, who have zero potential experience. The coefficient is close to 1 which indicates that new entrants are affected by overall labor demand variations similarly to the rest of the labor force. Moving down the “Employment” column, the coefficients decline until becoming insignificant over the first 4 years of the work life, which suggests a relatively quick recovery from graduating in a bad labor market (and vice-versa). This stands in contrast to the persistent scarring effects found in the literature, which tend to decay only around a decade into the work life (Oreopoulos et al. (2012), Kahn (2010a)).

More the become after 6 in the ca- puzzling, coefficients significantly negative year reer profile. These estimates imply that individuals who graduate in a bad labor market (i.e. characterized by low employment rates) end up exhibiting higher employment rates 6 into their careers than those who started in a better labor market. As noted in years Oreopoulos et al. (2012), these coefficients capture the effects of the initial employment conditions augmented by the regular evolution of employment conditions faced afterwards. Therefore, the first possible interpretation of these results is that cyclical variation in labor market conditions eventually dominates scarring effects. Cohorts that enter a bad labor market end up profiting from eventual economic recoveries (and vice-versa), and are not strongly penalized by having a bad start. Indeed, the youth employment rates and GDP growth cycles depicted in Figure 2 exhibit peak-to-trough durations of around 6 years.

experience are included in the sample.

15
Note also that, due to the structure of our data, the coefficients on higher years of potential experience rely on fewer cohorts for identification. Therefore, our results may reflect the specific circumstances of cohorts that graduated between 2006 and 2010. These cohorts contribute the most to the coefficients for higher numbers of years of potential experience. Indeed, our national model (see section 4.2), which leverages information from many more cohorts (graduation years 1996-2019), does not exhibit the same consistently negative coefficients at higher years of potential experience.

Conditional on being employed, we now examine the fraction of those employed in “good” jobs, as measured by the outcomes listed earlier (tables 4 and 5). In the first few years following labor market entry, if the prevailing employment rate was high, individuals are more likely to have a full-time job and work more hours, to be a wage worker, but also more likely to be employed in agriculture. The corresponding coefficients on our classic measures of informality such as the existence of a contract or social security coverage are also positive but not significant.<sup>7</sup> Starting in the 6th year of potential experience, these variables exhibit the same sign reversal described earlier when discussing employment rates. Those who faced higher employment rates when they exited school are less likely to have social security coverage, be employed full time, have a contract, to be wage workers, and, marginally, to be employed in the public sector. These patterns suggest that scarring effects in Egypt are short-lived in terms of job quality and are dominated by the cyclicality of labor market conditions.

It is worth mentioning that these patterns in employment quality could also be generated by a model of queuing in which applying for a good job restricts contemporaneous employment opportunities. Fluctuating initial employment opportunities would affect the cost of and lead some cohorts to for less opportunity queuing queue quality jobs than others. These cohorts would then exhibit both higher initial employment rates and lower employment quality down the road. However, since the same patterns apply to the quantity as well as the quality of employment, we favor the cyclicality hypothesis

> 7We do not detect significant associations with the wage levels earned by wage workers, the level of public employment, or with the size of the employing establishment.

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outlined above.

# **4.2 National model**

In this section we assess the robustness of our main results using a set of additional proxies for initial labor market conditions that span a longer time frame but are only available at the national level. These include the youth unemployment rate, the GDP growth rate, and the logarithms of oil and wheat prices. Each of tables 6 through 9 shows regression estimates for a representative selection of outcomes, each column corresponding to one of the four labor market condition proxies. Results for other outcomes are relegated to the appendix for legibility. In the national model, we do not exploit differences in labor market conditions across region, but instead rely on differences across years in which successive cohorts enter the labor market.<sup>8</sup> The advantage of these national level independent variables is that they are available from 1990 onwards. This allows us to include cohorts of workers that entered the labor market before 2006 in our sample.

The patterns in employment rates (table 6), and hours worked (table 7) are consistent with the results from the model. To the extent that are regional coefficients significant, they are positive for the first few career years, and negative afterwards (or the reverse when considering commodity prices).

Measures of employment quality such as social security coverage, full-time employment, having a contract, and public employment are for the most part negatively correlated with graduating in a period of high youth employment and gdp growth (table 8, and appendix tables A1 A2, A3). These results are also consistent with those from the regional model for the second half of the career profiles. The two models diverge in that the regional model produced positive correlations for the first few years of work prior to the sign switching. Working for a wage also exhibit the same negative correlations in both the regional and national models for most of the potential experience levels (table A4). However, the national and regional results are at odds for the first two years and the last three

> 8Variation across schooling levels also contributes to identification in both the regional and national

models.

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years of potential experience. The size of the establishment, conditional on working does not exhibit strong significant patterns in the national model (table A5). Working in agriculture (table A6) also does not exhibit strong correlations with either youth employment or GDP growth. However, it is negatively correlated with initially high oil and wheat prices for the first five years of potential experience and positively correlated thereafter.

The results on log-monthly wages (table 9), which are only defined for wage workers are also broadly consistent with the overall pattern found in the regional level model, in which good initial economic conditions are associated with positive outcomes early in the career, and negative outcomes down the road. This is reflected in the negative and significant coefficients attached to the interaction of potential experience levels above 5 and GDP growth and youth employment. Correspondingly, the coefficients for log-oil prices and log-wheat prices (which proxy bad labor market conditions) are negative.

Table 6: Results with national economic condition indicators as control variables

|Dependent Variable|Empl|oyment|||
|---|---|---|---|---|
|0b.pot<br>~~e~~xp#c.indepvar|0.216**|-0.0465|0.0122|0.00199|
|1.pot<br>exp#c.indepvar|0.313***|0.0132|0.0206|0.00487|
|2.pot<br>exp#c.indepvar|0.123|-0.0269|-0.0387|0.0117**|
|3.pot<br>exp#c.indepvar|-0.376|-0.0663***|-0.105**|0.0131**|
|4.pot<br>exp#c.indepvar|-0.508***|-0.0219|-0.0734***|0.00482|
|5.pot<br>exp#c.indepvar|-0.479*|-0.0113|-0.0490**|0.00201|
|6.pot<br>exp#c.indepvar|-0.0636|0.0227|0.00276|0.000374|
|7.pot<br>exp#c.indepvar|-0.342|-0.00259|-0.0216|-0.00734|
|8.pot<br>exp#c.indepvar|0.0871|0.0219*|0.0119|-0.00887*|
|9.pot<br>exp#c.indepvar|0.0672|0.0135|-0.0411|-0.0220***|
|10.pot<br>~~e~~xp#c.indepvar|-0.0211|0.0352***|0.0119|-0.000145|
|indepvar|0.00349**|0.0335**|-0.00137|0.00570***|
|cohort|-0.00505***|-0.00943***|-0.00775***|-0.00774***|
|# obs|15,822|15,822|15,822|15,822|
|R-squared|0.499|0.499|0.497|0.497|
|Independent Variable|youth<br>~~e~~mployment|log<br>~~o~~ilprice|log<br>~~w~~heatprice|Gdp<br>~~g~~rowth|

Notes: The dummy variable of being employed or not is regressed in each column on country level economic condition indicators at the year of graduation: youth employment rate (15-24 years old), oil price, wheat price and gdp growth; interacted with potential years of experience 0 to 10. The potential experience is captured by the difference between the individuals’ age at the year of the survey and their age at the year of graduation. (ii) Governorate and potential experience fixed effects are included in each regression. Controls for eudcational attainment are also included. (iii) Sample of males who graduated between 1990-2019. Data are constructed from LFS 2006-2019, and the economic conditions indicators are extracted from World Bank data.

(iv) Cluster-robust standard errors in parentheses (clustering on cohort and governorate of residence). (v) * , ** and ***refer to significance at the 10, 5 and 1% levels respectively.

18
Table 7: Results with national economic condition indicators as control variables

|Dependent Variable|Hours Wor|kedper week|||
|---|---|---|---|---|
|0b.pot<br>~~e~~xp#c.indepvar|16.84|-4.744|-10.03*|1.326|
|1.pot<br>exp#c.indepvar|39.48*|6.124|-0.474|3.324***|
|2.pot<br>exp#c.indepvar|36.57|8.542*|1.150|3.359***|
|3.pot<br>exp#c.indepvar|44.60**|5.216|-2.523|2.705***|
|4.pot<br>exp#c.indepvar|31.40|4.094|-5.099|1.664|
|5.pot<br>exp#c.indepvar|37.86|2.767|-1.292|-0.289|
|6.pot<br>exp#c.indepvar|13.67|0.395|1.812|-2.570**|
|7.pot<br>exp#c.indepvar|-27.76|3.466|2.592|-2.398*|
|8.pot<br>exp#c.indepvar|52.96|5.806|3.883|-1.260|
|9.pot<br>exp#c.indepvar|-16.79|3.064|-1.564|-1.115|
|10.pot<br>~~e~~xp#c.indepvar|-91.94***|-1.493|-0.789|0.669|
|indepvar|2.331***|17.05***|37.30***|-2.427***|
|cohort|3.446***|1.160***|1.291***|0.748***|
|# obs|15,822|15,822|15,822|15,822|
|R-squared|0.671|0.528|0.681|0.507|
|Independent Variable|youth<br>~~e~~mployment|log<br>~~o~~ilprice|log<br>~~w~~heatprice|Gdp<br>~~g~~rowth|

Notes: (i) Hours worked per week for employed individuals are regressed in each column on country level economic condition indicators at the year of graduation: youth employment rate (15-24 years old), oil price, wheat price and gdp growth; interacted with potential years of experience 0 to 10. The potential experience is captured by the difference between the individuals’ age at the year of the survey and their age at the year of graduation.

(ii) Governorate and potential experience fixed effects are included in each regression. Controls for eudcational attainment are also included.

(iii) Sample of males who graduated between 1990-2019. Data are constructed from LFS 2006-2019, and the economic conditions indicators are extracted from World Bank data.

(iv) Cluster-robust standard errors in parentheses (clustering on cohort and governorate of residence).

(v) * , ** and *** refer to significance at the 10, 5 and 1% levels respectively.

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Table 8: Results with national economic condition indicators as control variables

|Dependent Variable|Employe|d with social se|curitycoverage||
|---|---|---|---|---|
|0b.pot<br>~~e~~xp#c.indepvar|-0.386***|-0.0323***|-0.0907***|-0.00185|
|1.pot<br>exp#c.indepvar|-0.152*|0.0388***|0.0449*|-0.00530*|
|2.pot<br>exp#c.indepvar|-0.156**|0.0142|0.0158|-0.00580***|
|3.pot<br>exp#c.indepvar|-0.137|0.0159|0.0215|-0.00976**|
|4.pot<br>exp#c.indepvar|-0.0324|0.00392|0.0237**|-0.00420*|
|5.pot<br>exp#c.indepvar|0.204*|0.00256|0.0326***|-0.00148|
|6.pot<br>exp#c.indepvar|0.268**|-0.00287|0.0249**|0.00210|
|7.pot<br>exp#c.indepvar|0.133|0.00232|0.0193**|0.00380|
|8.pot<br>exp#c.indepvar|-0.194|-0.0135|-0.00925|-0.000245|
|9.pot<br>exp#c.indepvar|-0.240**|-0.0222*|-0.0296**|-0.00347|
|10.pot<br>~~e~~xp#c.indepvar|-0.278**|-0.0475***|-0.0420***|-0.00839***|
|indepvar|-0.00162|-0.0108|-0.0359*|-0.000769|
|cohort|-0.0129***|-0.00853***|-0.0116***|-0.0106***|
|# obs|14,311|14,311|14,311|14,311|
|R-squared|0.819|0.819|0.819|0.818|
|Independent Variable|youth<br>~~e~~mployment|log<br>~~o~~ilprice|log<br>~~w~~heatprice|Gdp<br>~~g~~rowth|

Notes: The dummy variable of being employed with social security coverage or not is regressed in each column on country level economic condition indicators at the year of graduation: youth employment rate (15-24 years old), oil price, wheat price and gdp growth; interacted with potential years of experience 0 to 10. The potential experience is captured by the difference between the individuals’ age at the year of the survey and their age at the year of graduation. (ii) Governorate and potential experience fixed effects are included in each regression. Controls for eudcational attainment are also included.

(iii) Sample of males who graduated between 1990-2019. Data are constructed from LFS 2006-2019, and the economic conditions indicators are extracted from World Bank data.

(iv) Cluster-robust standard errors in parentheses (clustering on cohort and governorate of residence).

(v) * , ** and ***refer to significance at the 10, 5 and 1% levels respectively.

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Table 9: Results with national economic condition indicators as control variables

|Dependent Variable|Log(Mo|nthlyWage)|||
|---|---|---|---|---|
|0b.pot<br>~~e~~xp#c.indepvar|0.522|0.0442|0.0278|0.0169|
|1.pot<br>exp#c.indepvar|0.217|-0.000762|0.0614|0.0227**|
|2.pot<br>exp#c.indepvar|-0.187|-0.0494*|-0.0550|0.00980|
|3.pot<br>exp#c.indepvar|0.539**|0.0581***|0.0682*|0.00162|
|4.pot<br>exp#c.indepvar|0.282|0.0391**|0.0875**|-0.00457|
|5.pot<br>exp#c.indepvar|0.370|0.0457**|0.0867**|-0.0149**|
|6.pot<br>exp#c.indepvar|0.300|0.0548**|0.0898***|-0.0146**|
|7.pot<br>exp#c.indepvar|0.0182|0.0493***|0.0908***|-0.0110**|
|8.pot<br>exp#c.indepvar|-0.118|0.0452**|0.0828**|-0.00271|
|9.pot<br>exp#c.indepvar|-0.279|0.0487**|0.0721***|-0.00585|
|10.pot<br>~~e~~xp#c.indepvar|-1.943***|-0.0834**|-0.176***|-0.0284***|
|indepvar|0.00145|0.0404*|0.0746**|-0.0186***|
|cohort|0.115***|0.113***|0.111***|0.110***|
|# obs|13,415|13,415|13,415|13,415|
|R-squared|0.884|0.885|0.885|0.885|
|Independent Variable|youth<br>~~e~~mployment|log<br>~~o~~ilprice|log<br>~~w~~heatprice|Gdp<br>~~g~~rowth|

Notes: Log of the individual’s monthly wage is regressed in each column on country level economic condition indicators at the year of graduation: youth employment rate (15-24 years old), oil price, wheat price and gdp growth; interacted with potential years of experience 0 to 10. The potential experience is captured by the difference between the individuals’ age at the year of the survey and their age at the year of graduation.

(ii) Governorate and potential experience fixed effects are included in each regression. Controls for eudcational attainment are also included.

(iii) Sample of males who graduated between 1990-2019. Data are constructed from LFS 2006-2019, and the economic conditions indicators are extracted from World Bank data.

(iv) Cluster-robust standard errors in parentheses (clustering on cohort and governorate of residence).

(v) * , ** and ***refer to significance at the 10, 5 and 1% levels respectively.

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# **5 Conclusion**

We find that cohorts of Egyptian men who exit school when labor market conditions are bad do not exhibit the same persistent scarring effects identified in rich countries (e.g. Kahn (2010b), Oreopoulos et al. (2012)). Instead, they are worse off for at most a few years and only in terms of the quantity but not the quality of employment. More puzzling, good initial conditions are in fact associated with negative outcomes after 5 or 6 years of potential experience. For example, those who faced a better labor market when they exited school (as measured by high employment rates) are less likely to be employed, to have social security coverage, to have a full time job, to have a contract, to be wage workers, and they work fewer hours, especially towards the end of their first decade of labor market experience.

We that these results that variation in labor market con- hypothesize reflect cyclical ditions dominates scarring effects. Cohorts that enter a bad labor market end up profiting from eventual economic recoveries, and are not strongly penalized by having a bad start. The negative correlation between initial employment rates and eventual employment quality could also be consistent with a model of queuing in which some cohorts forgo initial employment opportunities in order to secure rationed quality jobs down the road (and vice versa). Data sets spanning longer periods would be required in order to confirm our results on employment outcomes after year 6 and discriminate between these different hypotheses.

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