Social Conflict and a Migrant Youth Bulge in Urban Sub-Saharan Africa

Ashira Menashe-Oren

ABSTRACT

Sub-Saharan Africa (SSA) is the region of the world with the greatest expected future population

growth and where urbanisation is expected to increase the most in the coming decades. This has

raised legitimate concerns about the potential role of large cohorts of young men contributing to

social unrest and conflict. Prior work has ignored the distinction between youth bulges arising from

natural increase as compared to rural-to-urban migration. This paper uses a novel approach to

distinguish these factors in SSA between 1990 and 2014 and to examine the specific effects of a

migrant youth bulge on the likelihood of social conflict in urban SSA. Empirical analysis suggests that a

migrant youth bulge (YB) does not increase the probability of urban violence. However migrant YBs

may lead to an increased frequency of urban social conflict, specifically relating to ethnicity-religion

and elections. An overall disassociation between urban social conflict and migrant YBs suggests that

concerns over the role of large cohorts of young men in violent conflict may be unwarranted.

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INTRODUCTION

Empirical analysis on the effect of youth bulges on intra-state conflict, particularly in urban settings,

has led to mixed results. Some findings have shown that male YBs are not associated with social

disorder in cities in Asia and Sub-Saharan Africa (Urdal and Hoelscher 2009). Likewise, increasing urban

population pressure does not lead to a higher risk of social disorder (Buhaug and Urdal 2013). Other

findings indicate that riots are more likely where YBs coincide with greater levels of urban inequality

(Urdal 2008). The argument in this paper is that urban YBs can be produced by either combinations of

fertility and mortality during the demographic transition or by large flows of rural-to-urban migrants.

This paper questions whether the composition of youth bulges is an important factor which has

previously been ignored, building on research on the role of urban growth as well as YBs in social

disorder and violent conflict (Buhaug and Urdal 2013; Cincotta et al. 2003; Urdal 2008; Urdal and

Hoelscher 2009). Principally, if the urban youth bulge is comprised of a large proportion of rural-to-

urban migrants would we see increased conflict?

Causes of Conflict

The causes of intra-state conflict range from poverty and institutionally weak countries to ethnic

identification and fractionalisation (Bhavnani and Miodownik 2008; Collier and Hoeffler 2004; Esteban

et al. 2012; Fearon and Laitin 2003, 2011). Civil conflict is also affected by geography and location and

resource scarcity (Buhaug and Gates 2002; Buhaug and Rød 2006; Cincotta et al. 2003; Fearon and

Laitin 2003). Evidence of a neo-Malthusian link between environmental destruction accompanied by

population growth and conflict is mixed (Buhaug and Urdal 2013; Gleditsch and Urdal 2002; Urdal

2008). However, population is an important factor that interacts and overlaps with other causes of

conflict (Goldstone 2002).

A particular population phenomenon, a youth bulge, has been found to affect the likelihood of civil

conflict (Cincotta et al. 2003; Goldstone 2002; Mesquida and Wiener 1999; Staveteig 2005; Urdal

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2008; Urdal and Hoelscher 2009; Yair and Miodownik 2014). Two particular methods have been most

commonly used to operationalize the YB. One is as the proportion of young adults in a population,

often measured as the number of 15 to 24 year olds of the total adult population. The other is a YB as

a relative cohort, where large relative cohorts make it easier for conflicts to erupt (Staveteig 2005).

Described as an incendiary factor of the Arab Spring (Hvistendahl 2011), the YB is an enabling factor to

violent conflict.

The focus on young adults is based on a diverse literature claiming that young adults are relatively

easily mobilised, with fewer responsibilities to families and careers – frequently not yet married and

not fully integrated into the job market (Mesquida and Wiener 1999). The opportunity costs for

political violence are low, especially amongst large cohorts (Macunovich 2000). According to

Easterlin’s relative cohort size hypothesis, when a relatively large cohort comes of age economic

frustrations emerge- from strains on the education system, unemployment and reduced wages. This

may in turn enable political instability and armed conflict (Staveteig 2005). With high unemployment

and lack of opportunities, especially in the formal work sector, the alternative costs to engaging in

violent action are low (Collier and Hoeffler 2004). Turning to civil conflict can also be considered a

legitimate way to redress perceived economic, political and social inequalities when there is little to

lose. Young adults may be alienated and marginalised (Sommers 2010), politically excluded or with

unmet expectations. Deprived youth may aspire for something better and thus be motivated to take

action (Pinard 2011).

Notably, when referring to a youth bulge, it is most often a male YB. Proportionately large male youth

cohorts have been found to have a significant effect on regime type and change, with democracies

more likely to collapse (Weber 2013). Similarly, male youth bulges affect the frequency and severity of

conflicts (Mesquida and Wiener 1999). Men are more susceptible to violence from a behavioural

ecology perspective, as they strive for mate acquisition (Mesquida and Wiener 1999), and have

greater taste for risk (Wilson and Daly 1985). This is also evident by the accident hump in male

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mortality profiles by age where young adult men have higher mortality rates (Hannerz 2001).

Furthermore, skewed sex ratios (a high proportion of men to women in a population) may pose a

security threat with vast implications on marriage markets, drug use, crime and prostitution (den Boer

and Hudson 2004; Dyson 2012).

Migration and Urban Youth Bulges

Urbanisation is a result of the demographic processes of mortality, fertility and migration. Some

studies show that the primary component of urbanisation is urban natural increase (Preston 1979),

while many find that urban growth is predominantly due to rural-to-urban migration (Keyfitz 1980;

Rogers 1982). Urban age structures tend to be concentrated in the production and reproduction ages

during earlier stages of the demographic transition. The age selectivity of rural-to-urban migration

which occurs disproportionately amongst the young (Montgomery, 2003), and particularly amongst

males, reinforces an urban youth bulge.

The proportion of young migrants in the urban population may be fundamental to understanding the

effect of an urban YB on social unrest for a number of reasons. Firstly, migration may shift the ethnic

composition of urban populations and promote conflict (Fearon and Laitin 2011; Goldstone 2002). The

heterogeneity of urban areas can be a source of instability, when several ethnic, religious or regional

groups are in close social contact (Cincotta et al. 2003). Secondly, associated with urban growth, the

job market and economy may struggle to keep up with incoming migrants (Goldstone 2002). A surge

of young workers contributes to under employment and low wages. Also, coming from rural areas,

migrants are less educated than their urban counterparts (Sahn and Stifel 2002), making it harder for

them to find opportunities in the formal sector of the economy. Migrants are thus more likely to

experience economic marginalisation and relative deprivation (Gizewski and Homer-Dixon 1995). Low

paid migrants are peripheralised through market relations and excluded from different segments of

society (Cook 2015). Thirdly, migrants may feel alienation and marginalisation in cities (Cook 2015),

finding it hard to adjust socially and psychologically (Gizewski and Homer-Dixon 1995). Finally, by

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moving to the city migrants have greater opportunity for collective political action and mobilisation

(Gizewski and Homer-Dixon 1995), potentially being able to raise grievances that previously affected

them or continue to affect their rural family and community.

I hypothesise that a relatively higher proportion of young rural-to-urban migrants (a migrant YB)

overall increases the likelihood and frequency of social conflict in urban settings. In particular:

1) A large migrant YB neither increases nor decreases the likelihood and frequency of conflict

related to the economy, jobs and assets. Naturally these issues are mostly dealt with in strikes.

While it is expected that migrants have trouble in the job market, facing unemployment, it is

not expected that their economic marginalisation would be reflected in participation in strikes.

Labour strikes are generally held by people employed in the formal sector. Nonetheless, a

large proportion of migrants may create more competition for non-migrants in the job market,

leading to non-migrant dissatisfaction.

2) A large migrant YB increases the likelihood and frequency of conflict related to ethnic

discrimination and religious issues. Considering migrants are typically from different ethnic

groups and that cities are often ethnically heterogenic, violent urban conflict may evolve when

one ethnic group has a grievance against another (Esteban et al. 2012; Fearon and Laitin 2011;

Higashijima and Houle 2017).

3) To a lesser extent, a large migrant YB may increase the likelihood and frequency of conflict

related to human rights and democracy as well as elections. When migrants experience

relative deprivation, they may pertain to human rights (such as freedom of speech or rights to

trade), driving them to participate in such social conflict.

4) Finally, a large migrant YB is expected to increase somewhat the likelihood and frequency of

social conflict related to domestic war, violence and terrorism. Migrants may protest wars

fought in rural regions over natural resources, affecting their family and place of origin.

DATA AND METHODOLOGY

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The Social Conflict Analysis Database (SCAD) contains data on social conflict in 42 Sub-Saharan African

countries from 1990 to 2014, covering 7200 instances of protests, riots, strikes and government

related violence (Salehyan et al. 2012). 1 The data is based on systematic aggregation of press reports.

This has been found to be an appropriate means of analysing low intensity conflicts common in SSA

(Bocquier and Maupeu 2006). Politically significant conflict events are identified through keyword

searches of news wires. These events are mostly large-scale, drawing many participants, including

significant acts of violence and threatening to political stability. On average there are four conflict

events every year per country. The data is georeferenced and indicates whether the conflict was in

rural or urban locations.

The conflict data is merged with population data to see how urban youth bulges may affect conflict

occurrence, creating a cross-section time series dataset. United Nations Department of Economic and

Social Affairs’ (UN-DESA) Population Division data on urban and rural populations by age and sex -

URPAS (United Nations 2014a) - is used from 1980 to 2015 to calculate YBs for men for every five

years and for each country. The URPAS data provides best estimates of rural and urban populations by

age and sex, building on census data, population registers and demographic interpolation (United

Nations 2014b). The data also allows for measuring migration estimates using the census survival ratio

method (CSRM), to obtain a measure of the proportion of migrants among the urban YB.

The CSRM provides an approach to estimate migration flows between rural and urban areas (Hamilton

and Henderson 1944; Preston 1979), especially useful considering little data is available of such

migration in SSA (de Brauw et al. 2014). The standard CSRM approach estimates survivorship for each

age group between two censuses exactly ten years apart for the population as a whole. These total

cohort survival ratios are the backbone upon which the migration estimates are based. The

survivorship levels are adjusted for the urban population.2 The urban cohort survival ratios are then

1 SCAD does not include cases of conflict or violence that are coded as civil war in the Uppsala Armed Conflict

Database. 2 Urban survival is assumed to be 25% higher than rural.

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used to predict the expected number of people in each urban age group at the time of the second

census. The difference in the expected number of urban people and the actual number measured in

the second census provides an estimate of net rural-to-urban migration. I use an adjusted CSRM with

the URPAS data, estimating migration every five years, based on rural cohort survival ratios. This

approach is preferable and may be more robust in the SSA setting where urbanisation levels are low

(Menashe-Oren and Stecklov forthcoming ). Important limitations to the CSRM, valid for this adjusted

approach too, have been discussed elsewhere particularly regarding potential bias from international

migration and reclassification (Menashe-Oren and Stecklov forthcoming; Moultrie et al. 2013; Preston

1979). It is worth noting that the migration estimates are a combination of both net rural-to-urban

migration as well as reclassification, where formerly rural areas are redefined urban once they pass a

threshold of “urbaness”.

The CSRM produces rural-to-urban migration estimates for all 42 SSA countries with conflict data for

every five year interval between 1990 and 2015. The migration estimates are the annual net number

of rural-urban migrants – where a positive number indicates there were greater flows from the rural

to urban sectors than from the urban to rural. These migrants are relatively new in urban areas-

having moved on average two and a half years ago. Thus the proportion of migrants in urban

populations estimated relates to recent migrants. The proportion would be even higher if considering

migrants who moved earlier. A migrant YB is measured as the ratio of young adult migrants aged 15 to

24 to older working aged adults aged 25 to 59 in the urban sector.3

Regression models are utilised to predict the relative contribution of rural-to-urban migration as part

of the urban YB to cases of conflict. A generic version of the model is:

Pr(𝑦𝑖,𝑑) = 𝛼 + 𝑥𝑖,𝑡−10𝛽1 + 𝑧𝑖,𝑡𝛽2 + 𝑐𝑖,𝑡 + 𝜀𝑖,𝑡

3 The youth bulge has been measured in various ways - aged 15-29 or aged 15-24, as percentage of total adult

population or out of total population. I test for sensitivity and run the main model with a bulge defined by ages 15-29.

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where Pr(𝑦) is the outcome of interest - the probability of urban social conflict. 𝑥𝛽1 is a vector of the

key explanatory variables; depending on the model it represents the urban YB, the proportion of rural-

to-urban migrants aged 15 to 24 to adults in urban area aged 25 to 59 and the non-migrant urban YB.

𝑧𝛽2 is a vector of the control variables, c indicates the time-invariant fixed effects on countries and 𝜀

indexes residuals. The t signifies temporal effects and i country-level analysis. The models are run in

Stata 11 using fixed effects (FE) binary logit regression models. Fixed effects Poisson models are also

run when the outcome is the number of conflicts. Country FE models account for between-country

sources of heterogeneity and were chosen to control for time invariant variables such as ethnic and

religious fractionalisation and climate. A Hausman test indicated that a FE model is preferred to a

random effects model– the test was significant at 0.01.

The models are run for all social conflict cases in urban settings and are also separated according to

the main issue at the source of the conflict – 1) human rights and democracy, 2) economy, jobs and

assets, 3) domestic war, violence and terrorism, 4) elections, 5) ethnic or religious discrimination and

6) other or unknown issues including environmental degradation, foreign affairs and education. The

key explanatory variables (youth bulges) are lagged by five years to account for temporal dependence

between observations within countries. The models take into account time (1990 to 2015), country

level economic development, political regime, unemployment, urban population size (logged), percent

of population in urban settings and the urban youth sex ratio.

Economic development can predict conflict as a poor country is constrained in meeting the demands

of its citizens, while a wealthy country can easily distribute resources and dampen any dissatisfaction

(Fearon and Laitin 2003). It is measured as gross domestic product (GDP) per capita in $US

(International Monetary Fund 2014) – standardised to mean of zero and standard deviation of one

because of high variability. The effect of regime type is argued as a measure of weak governance;

established democracies or harsh autocracies are both less likely to experience conflict than unstable

regimes (Hegre et al. 2001). A democracy-autocracy score is taken from the Polity IV Project of the

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Centre for Systemic Peace and provides a convenient measure of general regime effects (Centre for

Systemic Peace 2015). The score ranges from -10 (strongly autocratic) to +10 (strongly democratic).

The mean polity score for African countries included in the analysis is 1.41. Although questionable,

unemployment is often cited as a strong case for people to engage in violence (Cramer 2011;

Goldstone 2002; Hvistendahl 2011) - from gang participation to civil wars, including low opportunity

costs of violence (Collier and Hoeffler 2004). Unemployment is measured as the percent of male

labour force unemployed (World Bank 2016). Population size can account for conflict when the larger

the population the greater likelihood of fractionalisation and the more recruits available. The

proportion of the population urban, like urban growth, can affect violent conflict (Cincotta et al. 2003;

Goldstone 2002; Wirth 1938), especially when interacting with other factors (Gizewski and Homer-

Dixon 1995). A sex ratio with higher proportions of men, can affect marriage markets and increase

undesirable behaviours (Dyson 2012). A youth sex ratio is measured as the proportion of 15-24 year

old males to females in the urban sector. The measures of population size, proportion urban and sex

ratios are based on the URPAS data. Ethnic and religious fractionalisation are not included in the

models as it is unvarying over the 25 year period analysed, but is controlled for in the FE models.

RESULTS

Initially, it is worth considering the dependent variable, probability and number of social conflicts, and

the main independent variables, the migrant YB and the non-migrant urban YB, for the main 1008

sample (42 countries over 24 years). The probability of at least one social conflict (on any issue) in an

SSA country is 0.72 over the whole period, ranging from a low of 0.45 in 1990 to a high of 0.9 in 2000.

The mean number of conflicts per year is 4.4, from 1.7 on average in 1990 to 11 in 2012.

Although analysis in this article is according to the main issue at the source of the conflict, these issues

are often dealt with by specific means including demonstrations, riots and strikes (spontaneous or

organised) and pro- anti- extra- or intra-governmental violence (grouped here as “other”). Table 1

maps the conflicts by issue and type of conflict. Urban conflicts regarding human rights and democracy

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are dealt with mostly through demonstrations (71%). Conflicts on economic issues are expressed

through strikes (36%) and demonstrations (34%). 47% of election related conflicts are demonstrations

and 30% are riots. Demonstrations are in general a preferred method of social conflict, composing

45% of all social conflict in SSA. As a means of advocacy or protest based on social networks and

organisation, demonstrations may be preferred to riots and strikes as they are largely more peaceful

(McPhail and Wohlstein 1983).

Table 1: Main issue of conflict and type of conflict

Human rights/ Democracy

Economy/ Jobs/ Assets

Domestic war/ Violence/ Terror

Elections Ethnic/ Religious

Other/ Unknown

Total

Demonstration 611 249 190 209 79 650 1,988 % 70.96 33.65 45.13 47.07 20.73 41.69 45.12

Riot 132 121 70 132 126 281 862 % 15.33 16.35 16.63 29.73 33.07 18.02 19.56

Strike 51 267 16 15 2 69 420 % 5.92 36.08 3.8 3.38 0.52 4.43 9.53

Other 67 103 145 88 174 559 1,136 % 7.78 13.92 34.44 19.82 45.67 35.86 25.78

Total 861 740 421 444 381 1,559 4,406 % 100 100 100 100 100 100 100

Turning to the key explanatory variables on population composition, the urban population has

proportionately more young adults (aged 15-29) than the rural population – 31% compared to 26%.

The urban population is distributed by age as seen on the left of Figure 1; on average in 2015, almost

27% of the urban population is under age ten and merely 3.6% over age sixty. 58% of the urban

population is of working ages (15-59). In contrast, on the right side of the pyramid in Figure 1 is the

age distribution of rural-to-urban migrants in SSA in 2015. The migrant population is less evenly

distributed between age groups and has high proportions of ten to twenty year olds – 44.4%. The

proportion of migrants in the urban population on average for SSA countries peaks for age groups 10-

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14 for women (25% of the urban population), and 15-19 for men (28% of the urban population), as

seen in Figure 2. Migrants are clearly an important feature of the urban YB.

Figure 1: Mean Male Urban and Migrant Population Composition for Countries in Sub-Saharan Africa

for 2015

Figure 2: Mean Proportion Migrants of Urban Population by Age for Sub-Saharan Africa 2015

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The mean male urban YB is 0.65, that is, 65% of the male adult working population is aged 15-24 over

all time periods and countries. The mean male migrant YB is 11%, ranging from -1% to 67%, and the

non-migrant YB is 55%, ranging from -1% to 89%. Between 1990 and 2013 the migrant and non-

migrant urban YBs have seen declines, reflecting changes in the urban population as it transits to

lower fertility.

When examining the effect of an urban youth bulge (not seperated into migrants and non-migrants)

on social conflict in SSA, results indicate that a male YB increases the odds of conflict (Table 2). An

increase in the urban YB significantly raises the odds of urban conflict by a factor of 44, when only

accounting for time (“Null Urban” model). When control variables of population size, proportion

urban, sex ratio, GDP, polity and unemployment are included in the model, “Urban OLS”, the odds

become insignificant. However, in a country fixed effects model, with an increase in the urban male YB

the odds of social conflict increase by a factor of 477 within countries. In this model (“Urban FE”) all

other factors included in the model are insignificant. In the OLS models when time-invariant effects

are not removed, population characteristics and unemployment significantly affect the probability of

conflict. An increase in population size raises the odds of conflict; a higher sex ratio, with

proportionately more men also increases the odds of social conflict. With an increase in the

proportion of a country’s urban population the chances of conflict decrease by around 80%. This may

be explained by urbanisation reflecting economic growth (Black and Henderson 2011; Eaton and

Eckstein 1997). Decomposed to migrant and non-migrant youth bulges, results indicate that within

countries a non-migrant male urban YB significantly increases the odds of conflict by a factor of 173.

The migrant YB has no significant effect on overall social conflict in SSA.4

4 Three other models tested for sensitivity of these results- using a non-lagged youth bulge, a youth bulge

defined as 15-29 year olds and a female youth bulge. None of these models indicated a significant effect of migrant youth bulges on conflict. It is worth noting however, that a female youth bulge reduces the odds of social conflict by 20%.

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Table 2: The Probability of Urban Conflict in 42 Sub-Saharan African Countries (Logistic Models)

Null Urban Urban OLS Urban FE Null Migrant

Migrant OLS

Migrant FE

Urban Male YB 44.338** 2.856 477.150*

(32.087) (3.162) (1201.345)

Male Migrant YB 2.558 0.157 5.537

(2.486) (0.213) (16.245)

Non-Migrant Urban Male YB 20.939** 1.348 172.741*

(14.191) (1.332) (382.535)

Year 1.032** 1.021 1.060 1.032** 1.024^ 1.062

(0.011) (0.014) (0.038) (0.011) (0.014) (0.041)

Logged Urban Population Size

2.143** 0.701 2.127** 0.798

(0.208) (0.731) (0.209) (0.899)

Proportion of Population Urban

0.192* 0.009 0.110** 0.002

(0.141) (0.051) (0.085) (0.010)

Urban Youth Sex Ratio 1.054** 1.022 1.057** 1.033

(0.015) (0.050) (0.015) (0.053)

GDP 0.888 1.231 0.869 1.131

(0.115) (0.336) (0.110) (0.300)

Autocracy-Democracy Score 0.977 1.008 0.971^ 1.017

(0.015) (0.033) (0.015) (0.033)

Unemployment 1.080** 0.993 1.083** 0.989

(0.020) (0.070) (0.020) (0.070)

Constant 0.000** 0.000^ 0.000** 0.000*

(0.000) (0.000) (0.000) (0.000)

No. of cases 1008 873 744 1008 873 744

Odds ratios (Standard errors)

Two-tailed test: ** p<0.01; *p<0.05; ^p< 0.1

Examining the probability of urban conflict by main issue facilitates a more refined view on an urban

YB effect on conflict. In Table 3, the central model is run according to the main issue behind the

conflicts. A male migrant YB significantly does not increase or decrease the odds of urban terrorism or

domestic war. Though insignificant, the coefficients suggest that a migrant YB lowers the probability of

human rights-democracy, elections and ethnic-religious related conflicts in the urban sector while

increases the odds of economic-job-asset and other conflicts. A non-migrant urban male YB increases

the odds of all urban conflicts regardless of the main issue behind the conflict (except for terror

related conflicts). In particular, the odds of an economic conflict are significantly raised by a factor of

221 with an increase in the non-migrant YB. A multinominal logistic model indicated that when

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controlling for country effects, neither a migrant nor non-migrant YB increased the relative risk of

conflict regarding one issue over another (results not shown).

Table 3: The Probability of Urban Conflict in 42 Sub-Saharan African Countries, by Main Issue of

Conflict (Logistic Models)

Human Rights

Economic Terror Elections Ethnic Other

Male Migrant YB 0.012 15.396 0.000* 0.011 0.005 10.671

(0.034) (42.205) (0.000) (0.036) (0.026) (27.550)

Non-Migrant Urban Male YB 49.425^ 221.385* 0.005 2.135 143.473 43.178^

(116.529) (509.590) (0.018) (5.887) (735.352) (84.558)

Year 0.977 1.054 1.119* 0.934 1.059 1.023

(0.037) (0.040) (0.050) (0.041) (0.070) (0.034)

Logged Urban Population Size 14.266* 0.497 0.191 2.803 6.558 1.352

(15.767) (0.578) (0.257) (3.562) (11.990) (1.328)

Proportion of Population Urban 0.000** 0.000^ 18205.080 31.876 0.000* 1.522

(0.000) (0.000) (176722.712) (218.196) (0.000) (8.468)

Urban Youth Sex Ratio 1.109^ 1.078 0.999 1.085 0.951 1.025

(0.061) (0.055) (0.097) (0.076) (0.109) (0.046)

GDP 1.912* 1.293 0.517^ 1.092 0.657 1.036

(0.553) (0.339) (0.206) (0.342) (0.336) (0.262)

Autocracy-Democracy Score 1.001 1.055^ 1.014 1.105** 0.941 0.972

(0.029) (0.030) (0.045) (0.038) (0.050) (0.028)

Unemployment 1.000 1.019 0.728* 0.933 1.069 0.949

(0.074) (0.074) (0.117) (0.075) (0.146) (0.068)

No. of cases 836 840 597 785 440 873

Odds ratios (Standard errors)

Two-tailed test: ** p<0.01; *p<0.05; ^p< 0.1

Tables 2 and 3 have shown that male migrant YBs do not affect the probability of conflict while the

non-migrant YB tends to increase the odds of urban social conflict. In Table 4 I examine whether the

YBs increase the number of conflicts in SSA. In the OLS model an increase in the size of the migrant YB

by one unit would lower the rate ratio of urban conflict by a factor of 0.026. This effect disappears in

the fixed effects model which accounts for between-country heterogeneity. The number of conflicts

within countries on election and ethnic-religious issues is reduced by over 99% with a unit increase in

the size of the migrant YB. An increase in the non-migrant male YB significantly increases the

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frequency of all conflicts within countries – in particular on issues of human rights-democracy,

economic-job-assets, elections and ethnic-religious.

According to the “All FE” model, as time progresses the incident rate is expected to change by a factor

of 0.98, suggesting a decline in the number of conflicts by 2% every year. In addition, the population

variables are all significant in accounting for the number of social conflicts: an increase in urban

population size increases the rate ratio of conflicts by a factor of 4.6; an increase in the proportion of

the population urban lowers the rate ratio of conflict by a factor of 0.008; and a unit increase in the

urban youth sex ratio suggests an increase of nearly 10% in conflicts. A positive relationship is also

found between GDP and the frequency of conflict.

Table 4: Number of Conflicts in 42 SSA Countries, by Main Issue of Conflict (Poisson Models)

All OLS All FE Human Rights FE

Economic FE

Terror FE Elections FE

Ethnic FE Other FE

Male Migrant YB 0.026** 0.393 0.092 1.051 0.046 0.014^ 0.005^ 0.915

(0.008) (0.254) (0.135) (1.581) (0.116) (0.034) (0.015) (0.979)

Non-Migrant Urban YB 0.042** 7.635** 22.079* 9.564^ 0.029^ 97.536* 2.40e+07** 2.346

(0.009) (4.406) (28.826) (12.093) (0.057) (189.419) (86114512.7) (2.325)

Year 1.022** 0.983* 0.953** 1.028 1.067** 0.924** 0.867** 0.988

(0.003) (0.008) (0.017) (0.019) (0.025) (0.024) (0.030) (0.013)

Logged Urban Population Size 1.937** 4.577** 33.106** 0.665 0.556 12.107** 254.549** 4.005**

(0.028) (1.116) (18.464) (0.417) (0.417) (9.710) (296.853) (1.660)

Proportion of Population Urban 0.291** 0.008** 0.000** 0.010 2.48e+05* 0.276 0.000** 0.514

(0.058) (0.012) (0.000) (0.034) (1256815.6) (1.203) (0.000) (1.298)

Urban Youth Sex Ratio 1.029** 1.099** 1.110** 1.107** 1.065 1.193** 1.059 1.080**

(0.003) (0.016) (0.033) (0.033) (0.066) (0.059) (0.099) (0.028)

GDP 0.881** 1.162** 1.508** 1.314* 0.713^ 0.865 1.850* 1.036

(0.027) (0.065) (0.206) (0.170) (0.141) (0.154) (0.522) (0.098)

Autocracy-Democracy Score 0.993^ 1.001 0.943** 1.033* 0.953* 1.092** 1.016 1.002

(0.004) (0.006) (0.012) (0.014) (0.020) (0.023) (0.026) (0.011)

Unemployment 1.009* 0.978 1.008 0.978 0.934 0.946 1.053 0.966

(0.003) (0.015) (0.040) (0.032) (0.063) (0.043) (0.065) (0.025)

Constant 0.000**

(0.000)

No. of cases 873 873 850 854 597 785 440 873

Incidence rate ratios (Standard errors)

Two-tailed test: ** p<0.01; *p<0.05; ^p< 0.1

16

DISCUSSION

Classic sociologists regularly describe urban life as fundamentally different to life in rural areas.

Amongst them, Wirth (1938) coherently defined urban life. He suggested that three key features of

the urban sector may lead to violence – population size, density and heterogeneity. These features are

associated with specialisation, utilitarian interpersonal relationships, increased competition and social

stratification which can all promote unrest and violence. Rural-to-urban migrants play a significant role

in increasing population size, density and heterogeneity. They are an important component of

urbanisation to contend with when considering urban conflict. Results have shown that migrants are a

particularly large proportion of young adults in the urban sector yet multivariate analysis suggests that

overall in SSA a migrant YB is not a contributing factor to social conflict. While a migrant YB doesn’t

increase the probability of urban violence, it may increase the number of conflicts, particularly ethnic-

religious and election related conflicts within countries.

My first hypothesis is essentially confirmed – migrant youth bulges are insignificant in explaining

economic related social conflicts. However, the coefficients are positive suggesting if any effect exists,

the migrant YB would increase the frequency and probability of economic conflicts within countries. In

light of the urbanisation of poverty, migrants may experience material deprivation and inequalities

forming a source of insecurity (Fox and Beall 2012). When faced with chronic poverty and difficulties in

the job market, the cost of engaging in violence may outweigh individual costs (Collier and Hoeffler

2004). Non-migrant YBs on the other hand are significant in increasing economic conflicts. Non-

migrants tend to engage in the formal job sector and thus participate in strikes.

My second hypothesis is partially confirmed – migrant youth bulges do not significantly increase the

odds of ethnic-religious conflict but they do increase the frequency of such conflict. Migrants

contribute to heterogeneity of the urban population. The ethnic heterogeneity of a population in itself

doesn’t necessarily lead to violence. It is the identification with the group, strengthened by between

17

group inequalities - a combination of political, economic and cultural inequalities - that increases the

number of conflicts (Higashijima and Houle 2017; Stewart 2010).

Results indicate that my third hypothesis is false. A migrant YB does not increase the likelihood and

frequency of conflict related to human rights, democracy or elections. Though mostly insignificant, a

migrant YB admittedly lowers the probability and frequency of such conflict. Unlike economic-related

conflict, the cost of participation in conflicts on human rights may not surpass individual grievances.

Finally, my fourth hypothesis is also falsified, a migrant YBs decreases the frequency of conflicts on

issues of domestic war and terror. Migrant YBs significantly do not raise or lower the likelihood of such

conflict. Domestic war and terror related conflicts likely affect the entire population equally.

CONCLUSION

The aim of this article is to disaggregate the urban youth bulge by distinguishing between natural

growth and migration. The study tests whether an increase in the relative proportion of young rural-

to-urban migrants increases the probability and frequency of social conflict by the main issue behind

the conflicts. In general, models failed to support the hypotheses. Although migrants do increase the

size, density and heterogeneity of the urban population and face relative deprivation and

marginalisation (Cincotta et al. 2003; Cook 2015; Fearon and Laitin 2011; Gizewski and Homer-Dixon

1995), two related factors may also be at play, compounding a relationship between a migrant YB and

conflict.

One, economic growth is sustained by urbanisation (Black and Henderson 2011; Eaton and Eckstein

1997), so rural-to-urban migration may positively reflect economic growth in cities. More young

migrants to the urban sector suggests better opportunities in education and the job market. Better

economic prospects may pull migrants to the city and allow them to integrate into the fabric of urban

society relatively smoothly. Two, corresponding to migration reflecting economic growth, urban areas

provide better opportunities for young adults. Despite increasing prevalence of urban poverty and

18

slums in Africa, urban populations are still better off than their rural counterparts (Awumbila et al.

2014; Oucho 2014). The urban setting provides more economic opportunities, better education (Sahn

and Stifel 2003) and better child health outcomes (Fink et al. 2014). For youth, cities also provide

anonymity, a resource for delaying adulthood expectations and reinvention, a space of possibility

(Sommers 2010). Young rural-to-urban migrants can relatively improve their position. Hence, any

grievances they may have are negligible to what has been gained by moving to the city. This has also

been suggested by Buhaug and Urdal (2013) who found that population growth in cities may even

lower urban disorder.

A number of challenges were encountered in this study. Firstly, while CSRM is a decent method for

measuring internal migration in SSA, these measures may be biased by international migration and

reclassification. All the same, considering the lack of other comprehensive sources of internal

migration data, this paper has managed to distinguish whether the population pressure of young

adults in urban settings is associated with migration or natural city growth. Additionally, although

migration may be related to relative deprivation, economic marginalisation and greater ethnic

heterogeneity, these factors lack disaggregated rural/urban quantitative data to include in analysis.

Further research is needed with improved data on youth exclusion to examine the underpinnings of an

urban YB, and in particular of a migrant YB. It is also important to expand the scope of future research

by examining the effect of a migrant YB on urban homicide rates and organised crime on the one hand

and civil war on the other.

This study represents a first exploration of the impact of migration on urban social conflict in Sub-

Saharan Africa. The relationship uncovered suggests that governments should seek to encourage

positive between-group relations and reduce inequalities along ethnic and religious divides as a means

of lowering the frequency of urban conflict. In addition, curbing urban unemployment, creating

opportunities for migrants and non-migrants alike as well as generally encouraging economic growth

in cities may lead to fewer cases of social unrest.

19

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