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Conceptualising and Measuring Social Cohesion in Africa: Towards a Perceptions-Based Index


Social cohesion is a key concept in development studies. Weak social cohesion is often related to slow economic growth and (violent) conflict. So far few attempts have been made to measure this complex concept in a systematic manner. This paper introduces an innovative method to measure national-level social cohesion based on survey data from 19 African countries. We distinguish three dimensions of social cohesion; i.e. the extent of perceived inequalities, the level of societal trust, and the strength of people’s adherence to their national identity. Importantly, our Social Cohesion Index (SCI) is based on individuals’ perceptions vis-à-vis these three different dimensions of social cohesion rather than certain macro-level ‘objective’ indicators such as GDP/capita or Gini-coefficients. We develop two social cohesion indices: a national average SCI and a Social Cohesion Index Variance-Adjusted (SCIVA); the latter one takes into account the level of variation across different ethnic groups within countries. The SCI and SCIVA are computed for and compared across nineteen African countries for the period 2005–2012 on the basis of Afrobarometer survey rounds 3, 4 and 5. We also investigate quantitatively the relationship between countries’ levels of social cohesion and the occurrence of a range of conflict events. As expected, we find that countries with low levels of social cohesion in a particular year according to our SCI are more likely to experience a range of different violent conflict events in the subsequent year.

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  1. 1.

    Generally, a positive association between what individuals perceive and what is actually observed has, indeed, been found. For example, using the Afrobarometer database we find that having more water, food and cash income is positively associated with perceiving one’s living conditions as more favourable.

  2. 2.

    Macro-level indicators of enclave living could be used to test relationships with social cohesion. We see this exercise being undertaken at the community level (cities, villages etc.) as enclave living is often limited to certain areas (e.g. major cities) of a country which might make it difficult to be picked up in nationally representative surveys. However, at the individual level there is already an established line of research focusing on contact theory: Does increased contact with other (cultural) groups also increase individual and intergroup trust? As trust is one of our major dimensions, these additional questions—currently not in the Afrobarometer—could be useful: e.g. how much do you engage with people from a different ethnicity, religion etc, and in what way (work, private life …). We could also expect a relationship between contact and the identity dimension.

  3. 3.

    This is similar to the inequality-adjusted Human Development Index of the UNDP. For more information:

  4. 4.

    This can be measured by the Cronbach coefficient (see e.g. Deafys et al. 2011; Hooghe 2012; Lord and Novick 1968).

  5. 5.

    It is important to note here that survey samples are sensitive to different types of errors and inaccuracies, including for example measurement error due to poorly designed questions or interviewer effects (see a.o. Biemer et al. 1991). Given that the questions in this paper are generally simple and transparent, it is unlikely that poorly design questions have introduced serious bias into the analysis. On the other hand, interviewer effects could potentially create a bias in the questions regarding identity. As indicated by Berinsky (2004) survey data collection constitutes a social interaction. However, to what extent such interviewer effects may have introduced significant variation is not known. For more information on Afrobarometer surveys see:

  6. 6.

    Designing ideal measurements for social cohesion requires quite elaborate empirical investigation. For example, we could see social cohesion as a second-level construct with inequality, trust and identity as first-level constructs. Each of the social cohesion dimensions could be measured with several items and analyzed via confirmatory factor analysis. The use of several items to measure our three dimensions of social cohesion would allow us to reduce measurement errors possible in some of the questions used here: the trust questions could be refined in terms of trust in whom (people from a different ethnicity, religion etc.) and in what situations (e.g. business transactions, private friendships), whereas nationality items could be designed to pick-up intergroup attitudes more precisely: in our country people should be educated in the dominant language; the existence of multiple cultural practices undermines/strengthens our nation etc. The difficulty in this approach is likely to lie in the necessity for our measurement items to have equivalent meanings in different countries (with different historical and cultural trajectories). Indeed, arguably the most difficult issue in designing cross-culturally comparable social cohesion indices lies in measuring the most politically salient cleavage in society: ethnicity, religion, migrants, caste etc. Furthermore, the use of multiple items might reduce errors in the measurements of our three dimensions, but a composite index of a limited number of items is generally much more straightforward to comprehend and analyze.

  7. 7.

    In the identity cluster, we restrict answers to individuals who feel a stronger affinity to a national identity than their own group identity. In a robustness test we also include those individuals who feel equally ethnic as national. The ranking of countries is similar to the one reported here. Also a similar variation over time is observed. More generally, we also perform robustness tests changing the cutoff points of all other questions (e.g. to include not just the extreme answer, but to include the two most extremes). Although minor differences can be observed, the overall ranking and variation over time seems to be a robust finding. This supports our belief that our index is a robust description of social cohesion.

  8. 8.

    Please note that we can only include countries for which we have data for more than one survey round. Hence even though the Afrobarometer Round 5 survey was conducted in 33 countries, in about 13 countries it was the first time that the Afrobarometer survey was conducted.

  9. 9.

    While the question related to ‘fair treatment’ is also framed around identities, this is not driving the significant correlation. Also a significant correlation exists between Identity and the ‘living conditions’ componenst of the inequality cluster.

  10. 10.

    This is also confirmed by the robustness results. Changing the way we compute SCI does not change the time-variation we observe. This suggests that variation over time is a key characteristic of SCI, and not a matter of measurement error.

  11. 11.

    This coefficient of variation, along with alternative measures of variation are reported in “Appendix 1”.

  12. 12.

    As measured as the one-year period after the last interview date in a specific national survey.


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We are grateful to Hiroyuki Hino for support and ideas, as well as to participants at seminars at the Kenya Institute for Public Policy Research and Analysis (KIPPRA) Nairobi and the University of Cape Town for their comments.

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Corresponding author

Correspondence to Arnim Langer.


Appendix 1

See Table 7.

Table 7 Variation in social cohesion across ethnic groups (alphabetical order)

Appendix 2: Definitions of ACLED Conflict Variables

The following event types are based on the ACLED dataset. Some event types are directly taken from the dataset, other types used in our analysis were constructed by limiting events to certain ‘actor types’.


ACLED defines a battle as “a violent interaction between two politically organized armed groups at a particular time and location.” Typically these interactions occur between government militaries/militias and rebel groups/factions within the context of a civil war. However, these interactions also include militia violence, rebel on rebel violence and military on military violence. There is no causality minimum necessary for inclusion.

The specific elements of that definition are as follows:

(1) A violent interaction is the use of armed force, including guns or military hardware, machetes, knives or any tool to inflict harm upon the opposing side.

(2) Organized armed groups including but not limited to rebel and government groups. (codebook 3, 2014, p. 9).

Depending on the outcome of a battle, ACLED distinguishes ‘Battle-No change of territory’, ‘Battle-Non-state actors overtake territory’, and ‘Battle-Government regains territory’. For the ‘Battle’ events used in our analyses, these types have been taken together.

State-Based Battle

This constructed event is a Battle which involves a government actor (Code 1 in the INTER1 or INTER2 variables).

Non-State Battle

This constructed event is a Battle which does not involve a government actor (Codes 2 (rebel force), 3 (political militia), 4 (ethnic militia) in the INTER1 and INTER2 variables).

Non-State Battle Between Communal Groups

This constructed event is a Battle which involves ethnic militias (Code 4 in the INTER1 and INTER2 variables).

Violence Against Civilians

Violence against civilians is defined as deliberate violent acts perpetrated by an organized political group such as a rebel, militia or government force against an unarmed non-combatant. These acts are political and harm or kill civilians, and are the sole act in which civilians are an actor. There is no minimum number of victims needed to qualify as an ACLED event.

Although the victims can be combatants in a different context, here they are UNARMED and NOT ABLE to defend themselves. One-sided violence also includes inflicting significant harm (e.g. bombing, shooting, torture, rape, mutilation etc.) or accosting victims (e.g. kidnapping and disappearances). It does not include incidents in which people are not physically harmed, (e.g. looting or burning, destruction of sacred spaces, and forced displacement.) (codebook 3, 2014, pp. 11–12).

Government Repression

Violence against civilians perpetrated by a government actor (Code 1 in the INTER1 or INTER2 variables).


A riot is defined as “a violent disturbance of the public peace by three or more persons assembled for a common purpose.” ACLED records reported information on both spontaneous and organized rioting. Organized riots can be planned by a previously recognized political group. The rioting group is not necessarily an inherently violent organization. A political party can riot (i.e. ZANU-PF in Zimbabwe). If the protesters or rioters are representing a group, the name of this group is recorded in the “ally” section. Spontaneous riots primarily involve civilians, without direct reference to an organized political group. Protests are nonviolent spontaneous organizations of civilians for a political purpose. Protesters do not engage in violence, and if violence occurs during a protest as a result of protesters “actions, this event is coded solely as a riot. If violence is done to protesters in the event of a protest, the event is coded solely as an act of ‘violence against civilians’”. (codebook 3, 2014, p. 11).


The Riots/Protests event type in ACLED is limited to Riots by only counting cases in which actors were defined as ‘Rioters’ (Code 5 in the INTER1 or INTER2 variables). Events are limited to the Riots/Protest event type to avoid double-counting as Rioters can also perpetrate ‘Violence against civilians’ in the dataset.


The Riots/Protests event type in ACLED is limited to Protest by only counting cases in which actors were defined as ‘Protesters’ (Code 6 in the INTER1 or INTER2 variables). Events are limited to the Riots/Protest event type to avoid double-counting as Protesters can also be the victim of ‘Violence against civilians’ in the dataset (e.g. in the case of government repression, see above).

Distinction Between External and Internal Events

All events were limited to events perpetrated by internal actors to ensure compatibility with the Afrobarometer surveys. The following procedures were followed:

For the event type “battle—no change of territory” data was browsed for 2 opposing national armies and/or police forces via the variables ACTOR1 and ACTOR2. These cases are dropped. This also includes national military forces battling mutinous forces of a foreign national army.

For all 3 battle categories, we filter out cases in which armed groups operate across the border of the country to which the group belongs. Government troops operating on foreign territory are considered as international cases, e.g. Mauritanese military in Mali; Congolese army in Uganda. However, events in which international actors (INTER1 or INTER2 = 8) assist in fighting an internal rebel group is considered as internal conflict (whether or not the government they are assisting is identified in the ALLY categories), e.g. the French military in Mali. If an organized rebel group (i.e. organization name such as Lord Resistance Army) operates outside of the country of origin, this is seen as international (e.g. Sudan’s liberation army operating in Uganda). Often we find ethnic militias or unidentified armed groups with a certain nationality between brackets. If the nationality of one of the armed groups differs from the country in which the event took place we decide that this is an international case.

For the category Riots/Protests we browse ACLED for events identifying one of the actors as “international” or with a nationality foreign to the country. This is based on the ACTOR variables or the NOTES variable. If foreign nationals protest or riot in a particular country, these cases are dropped. For the category “Violence against civilians”, we also browse ACLED for events identifying one of the actors as “‘international” or as having a nationality foreign to the country. If foreign civilians are victims of the violence, this is generally regarded as internal conflict (e.g. journalists, oil workers). However, if the perpetrators are not from the country where the event took place, the cases are dropped. Finally, as pirating can be regarded as an international crime, and as it is difficult to pinpoint the nationality of the pirates, all pirating cases are dropped.

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Langer, A., Stewart, F., Smedts, K. et al. Conceptualising and Measuring Social Cohesion in Africa: Towards a Perceptions-Based Index. Soc Indic Res 131, 321–343 (2017).

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  • Social cohesion
  • Measurement of multidimensional concept
  • Perceptions-based index
  • Africa