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Middle East and Africa

Male, Young and Educated. Profile of the Arab Demonstrator

A boy confronts the Egyptian Military Police south of Tahrir Square [Alisdare Hickson / Flickr]

After the uprisings of 2010-2011, protests have become a constant in various Arab countries. What are the demographic factors that determine participation in demonstrations? A quantitative analysis of the data gathered by the Arab Barometer identifies three main indicators: gender, age and level of education

This article was published in Oasis 31. Read the table of contents

Last update: 2022-01-24 15:04:11

 

After the eruption of the 2010-2011 uprisings, protests have become a constant in various Arab countries. What are the demographic factors that determine participation in demonstrations? A quantitative analysis of the data gathered by the Arab Barometer identifies three main indicators: gender, age and level of education. This contradicts the common narrative portraying the uprisings as movements composed by the poorest social groups against the richest.

 

Since the end of 2010, when the first street demonstrations erupted in Tunisia following the self-immolation of a young street vendor, the periodical occurrence of protest movements in the Middle East and North Africa (MENA) has been one of the region’s most recurrent features. In 2011, initial hopes for peaceful democratic transitions rapidly transformed into fears of increasing instability, civil wars, and Islamist takeovers.[1] For this reason, in the subsequent years part of the international community has supported—or simply turned a blind eye to—attempts by several of the region’s most powerful local regimes to stabilize the authoritarian status quo and forestall further instability.[2] However, such attempts have had limited success. Over the last decade, new waves of protests have continued to erupt, culminating in mass movements in 2019 that ended the decades-long presidencies of Omar al-Bashir (in Sudan) and Abdelaziz Bouteflika (in Algeria), and shackled the long-established political status quos in Lebanon and Iraq.

 

This paper aims to provide a rare example of quantitative investigation of the characteristics of protest participation in the Arab world over the last decade using the data provided by surveys collected in the third and fifth waves of the Arab Barometer. The time elapsed between these two waves—the first was collected in 2012 but refers to protests occurring in 2011, and the second was collected between the second half of 2018 and the end of 2019[3]—covers almost a decade since the Arab Spring began. Specifically, this paper analyzes the role of the demographic parameters included in the surveys as determinants of protest participation and their evolution from 2011 to 2019 with the aim of depicting a demographic profile of protesters from the region.

 

Methodology

 

This paper investigates the protest participation in seven MENA countries: Morocco, Algeria, Tunisia, Egypt, Sudan, Jordan, and Lebanon. Two criteria guided the choice of these case studies. First, data from these countries are present in both the third and fifth waves of the Arab Barometer survey. Second, all seven countries saw significant protest movements—either in 2011 or/and in the following years, especially in 2016 and 2018–2019.

 

The Arab Barometer surveys allow for classifying the interviewees according to their region (or province) of provenance in each respective country. In order not to overlook the significant economic, political, and cultural differences that often exist among different regions within each country, the determinants of protest participation were analyzed using 2-level multivariate logit regressions for the single countries (respondents–regions) and 3-level multivariate logit regressions for the analysis of the whole region (respondents–regions–countries). Participation in protests was used as the binary dependent variable,[4] while the demographic indicators included in the surveys were used as the independent variables. Such indicators are gender, age,[5] income level,[6] education level,[7] employment status,[8] and urban or rural place of residence.[9] Furthermore, another variable—corresponding to the survey question “Do you pray daily?”[10]— was added as a proxy for the individual level of religiosity.

 

To conduct the analysis at the country level, 2-level multivariate logit regressions, including all the above mentioned demographic indicators as independent variables, were used. Then, for the aggregated data from all the considered countries, 3-level multivariate logit regressions were used for each wave, using the same variables as both fixed and random effects.

 

Analysis of the Aggregated Data

 

A first strong element emerging from the analysis of the data is the trend of increasing protest participation over the decade under consideration. Figures 1 and 2 compares the shares of protest participants out of the whole samples of interviewees in 2011 and 2018-2019 at both regional and country level. At the regional level, the percentage of participants almost doubled (from 9% to 17%). Increases occurred in all the countries considered except Tunisia, where participation saw a slight decrease. Hence, although international media never emphasized the upheavals that occurred in Arab countries in the following years as they did for the 2011 Arab Spring, almost a decade later, the share of citizens claiming to participate in street protests has almost doubled. In Algeria, Morocco, and Lebanon, this percentage more than doubled between the two waves (in Lebanon, it is almost five times bigger and in Morocco and Algeria almost three times). Particularly impressive is the case of Egypt, where the draconian repressive measures introduced by the new regime established after the 2013 coup d’état[11] apparently could not prevent a slight increase in protest participation.

 

Graph 1 - Percentage of those who participated in the protests in the total sample of respondents

On the x-axis: the second and fifth surveys of the Arab Barometer. On the y-axis: the percentage of respondents who claimed to have participated in the protests at least once.

 

Graph 2 - Percentage of those who participated in protests by country

On the x-axis: the seven countries considered. Each column represents one of the two rounds of the Arab Barometer (left: third survey; right: fifth survey). On the y-axis: percentage of respondents who claim to have participated in street protests at least once.
 

Besides, the analysis of the demographic determinants at the aggregated level shows some change: only gender, education and age maintain significance throughout the two waves.[12] Their estimates confirm the hypothesis under discussion—namely, that male, educated, and younger-than-average individuals are most likely to take part in protests in Arab countries. No significant difference emerges between the two waves when comparing these predictors’ coefficients.

 

Moreover, while income and the urban-rural divide were significant predictors of participation in 2011, this is no longer the case for 2018–2019 (in the latter wave, the urban-rural divide is significant only within the 90% interval). The data show that income was positively related to participation in the 2011 protests, meaning that people belonging to the upper half of the income distribution were most likely to participate in the Arab Spring. This contradicts the common narrative depicting the uprisings as movements composed by the poorest social groups pitted against the richest.

 

When looking at the models that treat one independent variable at a time, other interesting elements arise. For instance, in both waves, being less religious is positively and significantly related to participation. This means that, although Islamist movements gained the upper hand in the aftermath of the 2011 uprisings, less religious individuals were those more likely to take part in the protests. This also contradicts a widespread perception of the 2011 movements, that saw Islamist movements as protagonists of both the uprisings and, especially, the following transitional periods.

 

Analysis by Country

 

While, at the aggregated level, education, gender, and age emerge as the sole significant determinants of participation throughout the two waves, analysis at the country level reveals a less uniform picture, with significant differences among the countries considered.

 

Algeria

 

The results of the multivariate regressions run on the data from Algeria for the third and fifth Arab Barometer waves show that this is the only case among those considered for which we have only one significant predictor in both waves—namely, gender. Being male is the only demographic characteristic associated with protest participation. In both waves, religiosity is significant only within the 90% interval; being less religious than average is related to participation, although weakly. Age (being younger than average) is also weakly significant in the first wave, as is unemployment in the second one.

 

Hence, in Algeria, participants in protests seem to come from a quite wide social spectrum, encompassing rather diverse social groups. The country witnessed significant protests in both 2011 and 2019. In 2011, protests were quelled after the government introduced generous economic measures to appease protesters. However, this strategy could not be repeated in the following years due to the sharp declines in oil prices and the consequent shrinking room for fiscal maneuver in government policies. Algeria witnessed a massive new wave of protest in 2019 that brought to an end Abdelaziz Bouteflika’s three-decade-long presidency. This new movement, called “Hirak,” continued throughout 2019, keeping pressure on the state leadership to implement reforms and was interrupted only by the outbreak of the Covid-19 pandemic.[13]

 

Morocco

 

In 2011, only age and the urban–rural divide emerged as significantly related to participation. With Algeria, Morocco is the only case in 2011 where education does not emerge as a determinant of participation. Hence, Moroccan protesters during the Arab Spring were likely to be younger than average and to come from urban areas. The latter element is not surprising since countryside areas have traditionally provided a strong support base for the Moroccan monarchy. However, the picture changed significantly in 2018–2019. Education and religiosity have become significant predictors of participation—both with positive coefficients, meaning that those who are on average more educated and less religious were more likely to participate—while the urban–rural divide lost significance. Combined with the great increase in the overall participation share in Morocco between the two waves, this may reflect increased participation of citizens from rural areas. In this regard, the emergence of the “Hirak al-Rif” (literally “movement of the countryside”) in the Berber-speaking regions of northern Morocco in 2016[14] may have contributed to a partial shift in the core of the protests away from the major urban centers.

 

Tunisia

 

Tunisia is commonly treated as the only successful democratic transition of the Arab Spring. After a three-year transitional period, the country introduced a new democratic constitution in 2014. The main Islamist party, Ennahda, which won the country’s first fully democratic elections, renounced its bid to form an exclusively Islamist government and has entered coalition governments with secular forces since 2011. After three successful general elections and two presidential elections, Tunisia’s new democratic institutional system seems to be consolidating. However, as data show, over the last decade the level of protest decreased only slightly. Issues related to economic growth and unemployment, especially in the poorer internal regions, are still major burdens for the country’s development and the stabilization of its new democratic system.[15] Over the last years, several reports of protests, especially in the southern regions, circulated.[16] Demonstrators have kept asking for more employment opportunities and interventions against the steady deterioration of the economic conditions of local households. Hence, the main factors behind the 2011 uprising seem not to have been properly addressed. This stagnant situation is reflected in the country’s data, which do not show significant differences between the two waves. As youth unemployment, especially among the most educated, is still the major issue facing the country, education and age, together with gender, emerge in both cases as the significant demographic predictors of participation.

 

Egypt

 

Massive street protests in 2011 forced then-president Hosni Mubarak to resign after 30 years in power. Since then, the country has undergone a decade of torment and turmoil. In the immediate aftermath of the protests, the Egyptian Muslim Brotherhood came to dominate the country’s political scene. Its party wing, founded shortly after the outburst Arab Spring, became the dominant force in the new parliament and in June 2012, Mohammed Morsi, one of its prominent members, became Egypt’s first freely-elected president. However, the coup d’état on July 3, 2013, abruptly ended the rise of the Brotherhood, bringing power back into the hands of the military. The head of the military, General Abdel Fatah al-Sisi, became the country’s new president in the following months. Since then, harsh measures have been introduced to repress any form of protest and dissent, targeting the Islamist opposition especially.

 

However, despite only limited reports of major protest movements emerging in the international media since 2013, the data show that the share of protest participation increased slightly compared to 2011. At the same time, there were minor shifts in the determinants of protest. For instance, while age (being younger than average) was not a significant predictor in 2011—within the 90% interval—it became a strong predictor in 2018–2019. This seems to confirm reports about protests that erupted briefly in September 2019, which were described as being driven by the youth, many of whom were even too young to have participated in the 2011 uprising.[17] Moreover, while income was positively related to participation in 2011, this relationship lost significance in 2018–2019. Now protesters seem to come from a broader socio-economic segment, including the poorer strata. Interestingly, while religiosity showed no significance in 2011, it is weakly significant in 2018–2019. This may reveal a slight rise in activism on the part of Islamist groups, such as the Muslim Brotherhood, in response to their repression and exclusion from the country’s political life. Education, gender, and the urban–rural divide are strong predictors in both waves, as educated males from urban areas seem to be the most likely demographic group to participate in protests.

 

Sudan

 

The country saw a radical variation in the determinants of protest participation between the two waves, alongside a dramatic increase in the participation share. While Sudanese joined the regional wave of demonstrations in Spring 2011, protests in the country remained rather sporadic compared to other MENA countries. Demonstrations took place in the capital Khartoum and a few other urban areas situated in regions inhabited by populations traditionally discriminated against by the central government.[18] Later that year, demonstrations almost completely ceased, while the nation’s attention was absorbed by the long-awaited secession of South Sudan, where most of the country’s oilfields were situated. In the following months, the country entered a deep economic crisis caused by the loss of its main sources of hydrocarbon revenues. To offset the widening budget deficit, austerity measures were introduced by the government over the following months, leading to a rapid deterioration in the population’s living conditions. In 2012 a new wave of protests began, this time also involving the Arab population of the main urban centers. Periodic waves of protests and instability continued throughout the following years, while the economy gave signs of recovery.[19] At the beginning of 2019, a new, massive protest movement finally led to the end of the 30-year-long presidency of Omar al-Bashir and the beginning of a transitional period dominated by a government jointly formed by the military leadership and civilian representants of the protest movement.

 

The data from Sudan clearly reflect the profound change sparked by South Sudan’s secession. In 2011, the data highlight the urban–rural divide (favoring rural areas) and religiosity (favoring less-religious-than-average individuals) as the main determinants of participation. Interestingly, among all the case studies considered in this paper, data from 2011 show that Sudan, along with Lebanon, is the only case in which gender is not significantly related to participation in at least one wave. Being male becomes significantly associated with participation in 2018/2019, while this time the urban–rural divide is no longer a strong predictor. In its stead, age (favoring younger-than-average citizens) has become significant.

 

Jordan

 

The country was among the first to see the emergence of a significant protest movement in the aftermath of Tunisia and Egypt’s uprisings.[20] However, protests in Jordan were relatively short-lived. In response to the demonstrations, King Abdallah appointed a new government charged with drawing up political reforms reflecting the protesters’ requests, although hardly any concrete legislative step was taken thereafter.[21] Observers pointed to the relative health of the country’s economy and the still-existing division and distrust between the Transjordanian and the Palestinian components of the society to explain the weakness of the movement. The following years have seen a steady deterioration of the country’s socio-economic conditions. To balance an increasingly unsustainable economic system, successive governments have introduced numerous austerity measures.[22] Several new protests erupted, especially in 2018 and 2019. However, the data show a slight decrease in the participation share between the two waves under consideration. The determinants of participation evolved significantly between the two waves. While in 2011, gender, education and income (favoring the upper half of the income distribution) were the sole variables significantly related to participation, the 2018–2019 wave saw age (favoring the younger-than-average individuals) and religiosity (favoring the less-religious-than-average individuals) also acquire significance. The urban–rural divide (favoring urban areas) also became weakly significant (within the 90% interval).

 

Lebanon

 

The country was not among the hardest hit by the 2011 wave of protests, as shown by the data in Figure 2. However, in the following years, economic deterioration and the socio-economic crisis sparked by the government’s corruption and mismanagement and the afflux of almost 2 million refugees from neighboring Syria ignited a new wave of protests against the political system established after the end of the civil war in 1990. Sporadic demonstrations with increasing participation have been registered since 2012.[23] In October 2019, a new package of austerity measures introduced by Saad Hariri’s government to combat Lebanon’s growing budgetary and financial difficulties sparked a new wave of protests of unprecedented proportions throughout the country.[24] Although the 2018–2019 Arab Barometer surveys were collected before the outburst of the latest protest movement, the data capture well the impressive increase in protest participation observed over the last years. However, there was no significant transformation in the determinants of participation. For both waves, age (favoring younger-than-average individuals) is the sole strong predictor of participation. Education and income (favoring poorer-than-average individuals) are only weakly related to participation for both waves. Interestingly, Lebanon emerges as the only country where the gender divide is never related to participation.

 

A still Ongoing Process?

 

This final section summarizes the results for each demographic determinant included in the analysis, in order to highlight continuities and exceptions in their influence over participation.

 

Gender. The enduring significance of the gender variable—in favor of male participation—is a characteristic of almost all the regressions run in this paper (except for Sudan’s first wave and Lebanon). The results reflect a patriarchal culture that curbs women’s participation in protest movements, an attitude that is still extremely widespread in the region. At the aggregate level, the gender determinant’s coefficient only slightly decreased between the two waves, suggesting that this gap is unlikely to disappear any time soon. Hence, since Arab women presumably harbour similar levels of discontent and grievances as their male counterparts, it is fair to conclude that, due to this cultural bias, current protest movements in Arab countries have so far failed to make effective use of a vast potential reservoir of additional participation.

 

Age. The data show that in both 2011 and 2018–2019, age has been a strong determinant of participation. Thus, the label of “youth revolutions” that the mainstream international media started using immediately after the Arab Spring’s inception is fitting. Younger generations in most Arab countries have been gravely hit by the economic deterioration of the last decades. For millions of young Arabs, the impossibility of securing adequate employment and income has resulted in the disruption of their life plans in terms of family formation and full access to adulthood.[25] Moreover, in some cases, such as Egypt, media accounts of the latest waves of protests, occurring almost a decade after the Arab Spring, show that most participants were too young to have taken part in the 2011 uprisings. Therefore, street protests are not a modality of contentious politics that has remained limited to the generations who took part in the 2011 uprisings. They are becoming a standard feature shared also by the new, younger generations who were not part of the 2011 movements.[26]

 

Urban–rural divide. Most of the reports about protests usually come from urban centers. The most powerful images of the 2011 uprisings were those coming from places such as Cairo’s Tahrir Square or Tunis’s Bourguiba Avenue. In fact, analysis of the data on protest participation shows that at the aggregated level the urban–rural divide was a significant determinant of participation only in 2011. At the country level, it was significant only in a few instances, such as Egypt (for both waves) and in Jordan (although weakly and only in the second wave). These results suggest the presence today of relevant—albeit thinly dispersed—protest movements in rural areas of most Arab countries that are significantly unreported and unresearched.

 

Education level. Together with age and gender, the importance of education level as a determinant of protest participation is the most consistent element emerging from the analysis. It was significantly linked to participation at the aggregate level in both waves and most country-level analyses. In part, this may stem from the fact that educated youth are among the most disadvantaged social groups in many Arab countries. Furthermore, this result confirms the findings of the literature on contentious politics, which tend to establish a direct and strong link between education and willingness (and capability) to participate in contentious actions.[27]

 

Income. The results related to the income variable contrast with the mainstream narrative on the Arab uprisings, which are often described as a struggle between poor and rich social groups. At the aggregate level, income emerges as a significant predictor of participation in the first wave, while losing significance in the results of the second one. Also, it is a significant predictor in several country-level analyses. In all instances belonging to the upper part of the income distribution (above the median) has emerged as positively—and not negatively, as the initial hypothesis asserted—related to participation. Such results confirm the more recent strands of literature on the influence of socio-economic status on protest participation, such as resource theory[28] and relative power theory,[29] which see economic status as crucial to determining whether individuals can afford to spend the time and the resources necessary to take part in protest movements. However, the fact that the income variable loses significance in the second wave may be explained by the dramatic increase in overall participation throughout the last decade. Participation seems to have also extended to marginalized social groups previously unwilling or incapable of taking part in protests, rendering income no longer a strong predictor.

 

Unemployment. Unemployment is probably the weakest predictor of protest participation among those considered in this analysis. It does not emerge as significant when considered with the other independent variables either at the aggregated level or in the country-level models.

 

Religiosity. The results regarding the role of individuals’ religiosity in determining protest participation are another intriguing aspect emerging from this analysis. In fact, especially in 2011, the emergence of Islamist movements as powerful (sometimes the most powerful) actors from the uprisings could have been interpreted as proof of the strong role played by religiosity in the protests against (usually) rather secular authoritarian regimes. Nevertheless, religiosity emerges as a non-significant predictor of participation at the aggregate level in both waves, while at the country level, its link in the 2011 surveys is always inverse, meaning that less religious people were more likely to participate. Perhaps even more surprisingly, such an inverse link emerged as less strong in the 2018–2019 wave. At the country level, we even find two cases (Egypt and Tunisia) in which higher religiosity is linked (albeit weakly) to participation, although Islamist movements do not appear to have played any meaningful role in protests in recent years, at least so far. In light of these results, the relationship between religiosity and participation in contentious politics in the MENA region needs more scrutiny.

 

In sum, it is possible to affirm that the core participants in the protests in both waves are educated, male citizens. Moreover, the data highlight a limited (although still significant in some instances) role for the other demographic variables that were considered, especially income. These results seem to confirm the literature defining the educated middle class as the most important social group when it comes to organizing and participating in contentious politics.[30] Finally, it is possible to affirm that the picture emerging from this analysis portrays a situation of persistent and increasing levels of contention throughout the region. According to numerous scholars, the dynamics that spurred the Arab uprisings in 2011 have all but disappeared.[31] On the contrary, they may only have been further exacerbated by the recent socio-economic crises caused by the coronavirus pandemic and the fall in oil prices. This may indicate that we are in the presence of an ongoing process of social and political transformation that may produce more contentious episodes, destabilization, and political change for years to come.

 

 

Tables

 

See the results of multivariate logistic regressions both at the aggregate level and by country.

 

Table 1 – Results of the 3-level multivariate logit regression model using the 2011 aggregated data from all considered countries

Fixed effects

       

Random effects

   
 

Estimate

Z value

Pr(>|z|)

   

Region:country variance

Education

0.29755

7.354

1.93E-13

***

Education

3.30E-02

 

Urban/Rural

-0.28874

-2.251

0.0244

*

Urban/Rural

2.51E-01

 

Gender

-1.25271

-8.221

< 2e-16

***

Gender

8.28E-01

 

Age

-0.01927

-4.877

1.08E-06

***

Age

6.07E-05

 

Religiosity

0.11139

1.749

0.0802

.

Religiosity

7.69E-02

 

Unemployment

0.09218

0.735

0.4622

 

Unemployment

3.23E-01

 

Income

0.57339

4.042

5.29E-05

***

Income

5.41E-01

 

*Significance codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

 

Table 2 – Results of the 3-level multivariate logit regression model using the 2018/2019 aggregated data from all considered countries

Fixed effects

     

Random effects

   
 

Estimate

Z value

Pr(>|z|)

   

Region:country variance

Education

0.24601

8.252

< 2e-16

***

Education

0.04324

   

Urban/Rural

-0.1671

-1.815

0.0695

.

Urban/Rural

0.20073

   

Gender

-0.8226

-10.562

< 2e-16

***

Gender

0.20282

   

Age

-0.0092

-2.941

0.00327

**

Age

0.00029

   

Religiosity

0.02326

0.72

0.47133

 

Religiosity

0.02353

   

Unemployment

-0.0131

-0.134

0.89338

 

Unemployment

0.11152

   

Income

-0.0771

-0.957

0.33866

 

Income

0.16524

   

*Significance codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

 

Table 3 – Results of the regression models using the 2011 data considering one variable at a time

Model 1 - Gender

 

Model 2 – Age

 

Model 3 - Urban/Rural

 

Estimate

-1.2432

 

Estimate

-0.0208

 

Estimate

-0.304

 

Pr(>|z|)

6.71E-13

***

Pr(>|z|)

2.14E-06

***

Pr(>|z|)

0.0392

*

Region:country variance

0.5713

 

Region:country variance

0.0002

 

Region:country variance

0.2473

 
                 

Model 4 - Religiosity

 

Model 5 – Unemployment

Model 6 - Income

   

Estimate

0.27711

 

Estimate

-0.5947

 

Estimate

0.9957

 

Pr(>|z|)

1.18E-05

***

Pr(>|z|)

4.49E-06

***

Pr(>|z|)

2.79E-11

***

Region:country variance

0.06491

 

Region:country variance

0.3249

 

Region:country variance

0.3735

 

*Significance codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

 

Table 4 – Results of the regression models using the 2018/2019 aggregated data considering one variable at a time

Model 1 - Gender

 

Model 2 – Age

 

Model 3 - Urban/Rural

Estimate

-0.8763

 

Estimate

-0.0211

 

Estimate

-0.242

 

Pr(>|z|)

<2e-16

***

Pr(>|z|)

3.64E-14

***

Pr(>|z|)

0.011

*

Region:country variance

0.2267

 

Region:country variance

0.00026

 

Region:country variance

0.2001

 
                 

Model 4 - Religiosity

Model 5 – Unemployment

Model 6 - Income

 

Estimate

0.11147

 

Estimate

0.24378

 

Estimate

0.23908

 

Pr(>|z|)

0.00073

***

Pr(>|z|)

0.00454

**

Pr(>|z|)

0.00344

**

Region:country variance

0.04258

 

Region:country variance

0.02293

 

Region:country variance

0.2506

 

 *Significance codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’

 

Table 5 – Algeria (2011 data)

 

Coeff

Pr(>|z|)

 

Education

0.00369

0.984

 

Urban/Rural

-0.1788

0.72195

 

Gender

-1.4624

0.00196

**

Age

-0.0393

6.82E-02

.

Religiosity

0.48483

8.38E-02

.

Unemployment

0.56338

0.23426

 

Income

-0.044

9.22E-01

 

 

Table 6 – Algeria (2018/2019 data)

 

Coeff

Pr(>|z|)

 

Education

-0.0039

0.9339

 

Urban/Rural

0.06572

0.8236

 

Gender

-0.58

3.22E-05

***

Age

0.00705

2.06E-01

 

Religiosity

0.1299

7.05E-02

.

Unemployment

0.31102

8.34E-02

.

Income

-0.2301

1.06E-01

 

 

Table 7 – Morocco (2011 data)

 

Coeff

Pr(>|z|)

 

Education

0.02891

0.7791

 

Urban/Rural

-0.9111

0.01128

*

Gender

-0.044

0.88208

 

Age

-0.0358

0.00789

**

Religiosity

-0.0002

0.99927

 

Unemployment

-0.3063

0.41529

 

Income

0.27467

0.42625

 

 

Table 8 – Morocco (2018/2019 data)

 

Coeff

Pr(>|z|)

 

Education

0.35101

< 2e-16

***

Urban/Rural

-0.1178

0.42332

 

Gender

-0.8704

1.65E-12

***

Age

0.01236

2.11E-02

*

Religiosity

0.18689

1.68E-03

**

Unemployment

0.05497

7.48E-01

 

Income

-0.1048

5.87E-01

 

 

Table 9 – Tunisia (2011 data)

 

Coeff

Pr(>|z|)

 

Education

0.29917

0.00011

***

Urban/Rural

-0.3687

0.0851

.

Gender

-1.7219

6.00E-14

***

Age

-0.0362

5.08E-07

***

Religiosity

-0.048

0.56212

 

Unemployment

0.04891

0.80161

 

Income

0.31774

0.14835

 

 

Table 10 – Tunisia (2018/2019 data)

 

Coeff

Pr(>|z|)

 

Education

0.40694

1.31E-10

***

Urban/Rural

0.11532

0.53174

 

Gender

-0.8809

4.61E-07

***

Age

-0.0174

7.77E-03

**

Religiosity

-0.1091

9.60E-02

.

Unemployment

-0.0566

8.35E-01

 

Income

0.06718

7.43E-01

 

 

Table 11 – Egypt (2011 data)

 

Coeff

Pr(>|z|)

 

Education

0.45978

3.37E-09

***

Urban/Rural

-0.5608

0.0187

*

Gender

-1.4494

4.79E-08

***

Age

-0.0134

1.07E-01

 

Religiosity

0.03472

8.09E-01

 

Unemployment

0.11262

0.6522

 

Income

0.21555

4.06E-01

 

 

Table 12 – Egypt (2018/2919 data)

 

Coeff

Pr(>|z|)

 

Education

0.25543

1.78E-08

***

Urban/Rural

-0.458

0.00217

**

Gender

-1.2433

< 2e-16

***

Age

-0.0213

5.43E-04

***

Religiosity

-0.1122

8.50E-02

.

Unemployment

-0.2149

4.17E-01

 

Income

0.15554

3.16E-01

 

 

Table 13 – Sudan (2011 data)

 

Coeff

Pr(>|z|)

 

Education

0.217457

0.00635

**

Urban/Rural

0.585528

0.02903

*

Gender

-0.23072

0.30923

 

Age

-0.01155

0.23516

 

Religiosity

0.371362

0.06736

.

Unemployment

-0.13768

0.59082

 

Income

0.354876

0.14848

 

 

Table 14 – Sudan (2018/2019 data)     

 

Coeff

Pr(>|z|)

 

Education

0.14716

0.00117

**

Urban/Rural

0.07572

0.61389

 

Gender

-0.6672

1.69E-07

***

Age

-0.01

1.32E-01

 

Religiosity

0.05608

3.77E-01

 

Unemployment

-0.2709

1.46E-01

 

Income

-0.1815

1.59E-01

 

 

Table 15 – Jordan (2011 data)

 

Coeff

Pr(>|z|)

 

Education

0.31427

0.00704

**

Urban/Rural

-0.223

0.54039

 

Gender

-1.2823

8.39E-04

***

Age

0.00616

5.83E-01

 

Religiosity

-0.0855

6.64E-01

 

Unemployment

-0.3326

0.30771

 

Income

1.23542

3.88E-03

**

 

Table 16 – Jordan (2018/2019 data)

 

Coeff

Pr(>|z|)

 

Education

0.18469

0.02439

*

Urban/Rural

-0.7045

0.06536

.

Gender

-0.7173

8.43E-04

***

Age

-0.0256

7.18E-04

***

Religiosity

0.16415

4.41E-02

*

Unemployment

0.28811

2.96E-01

 

Income

0.48624

2.21E-02

*

 

Table 17 – Lebanon (2011 data)    

 

Coeff

Pr(>|z|)

 

Education

0.13335

0.08825

.

Urban/Rural

-0.2739

0.44322

 

Gender

-0.1377

4.75E-01

 

Age

-0.026

2.07E-03

**

Religiosity

-0.0927

3.03E-01

 

Unemployment

-0.1118

0.76177

 

Income

-0.4511

7.77E-02

.

*Significance codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’

 

Table 18 – Lebanon  (2018/2019 data)

 

Coeff

Pr(>|z|)

 

Education

0.13335

0.08825

.

Urban/Rural

-0.2739

0.44322

 

Gender

-0.1377

4.75E-01

 

Age

-0.026

2.07E-03

**

Religiosity

-0.0927

3.03E-01

 

Unemployment

-0.1118

7.62E-01

 

Income

-0.4511

7.77E-02

.

 

 

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The opinions expressed in this article are those of the author(s) and do not necessarily reflect the position of the Oasis International Foundation

 


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[3] Depending on the country, the surveys were collected either in 2018 or 2019, or over a period spanning the two years.
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[5] Respondents were asked to write their age in numbers.
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[8] The 2012 surveys included the question “Do you work?” having the binary answer Yes/No. In the 2018–2019 surveys no such binary question was included. Instead, the closest query included seven possible answers: Employee; Self-Employed; Retired; Housewife; Student; Unemployed; Other. To normalize and make the two variables comparable, the latter query was transformed into a binary variable indicating whether the subject declared being unemployed or one of the other choices.
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To cite this article


Printed version:
Eugenio Dacrema, “Male, Young and Educated. Profile of the Arab Demonstrator”, Oasis, year 16, n. 31, pp. 53-64.


Online version:
Eugenio Dacrema, “Male, Young and Educated. Profile of the Arab Demonstrator”, Oasis [online], published on December 2020, URL: https://www.oasiscenter.eu/en/an-islamic-way-out-of-patriarchy