Identifying Determinants of Social Cohesion in African Countries – A Scoping Review Protocol
Creators: name: Wollburg, Clara
name: Leininger, Julia
Publisher: name: Open Science Framework
Year: 2022
Subjects: subject: Growth and Development
subject: African Studies
subject: Economics
subject: Social and Behavioral Sciences
subject: Inequality and Stratification
subject: Sociology
subject: FOS: Sociology
subject: Civic and Community Engagement
subject: International and Area Studies

description: 1) Introduction Social cohesion is an important tenet of peaceful and prosperous societies. Cohesiveness is linked to higher institutional quality, economic growth, and peaceful cooperation (Colletta &amp; Cullen, 2000; Easterly, Ritzen, &amp; Woolcock, 2006; Ferroni, Mateo, &amp; Payne, 2008). Moreover, a strong bond between members of society is connected to better quality of life and health outcomes (Kim &amp; Kawachi, 2017; Lippman et al., 2018; Papachristou, Flouri, Kokosi, &amp; Francesconi, 2019; Pattussi, Anselmo Olinto, Rower, Souza de Bairros, &amp; Kawachi, 2016). Social cohesion is therefore recognized as a central element of economic development and societal welfare by regional and national governments as well as by supranational political institutions (Chan, To, &amp; Chan, 2006). Although it has become common sense that social cohesion is important, its social, political, and economic drivers are not well understood. In particular, there is a limited understanding of the predictors of social cohesion in the African context. To address this evidence gap, this scoping review will summarize the quantitative evidence on factors that foster or impede social cohesion in African societies. 1.1) Defining social cohesion This review follows a definition of social cohesion that encompasses both vertical relations between members of society and horizontal relations between citizens and the state that hold societies together (Chan et al., 2006; Leininger et al., forthcoming). In particular, we define social cohesion in terms of three attributes: cooperation for the common good, trust, and an inclusive identity (Leininger et al., forthcoming). The attribute of inclusive identity refers to a positive identification and shared sense of belonging that bridges different groups and identities of a society (Leininger et al., forthcoming). This includes identification with wider social groups (social identity) and/or the nation state (state/national identity) (Chan et al., 2006; Leininger et al., forthcoming). Identifying exclusively with one’s social in-group, e.g. based on religion or ethnicity, can weaken overall social unity, when it leads to hostile attitudes and behaviors towards other groups of society (Langer, Stewart, Smedts, &amp; Demarest, 2017). Thus, we follow a definition of inclusive identity where wider social identification, e.g. based on nationality, is compatible with, but superordinate to, in-group identification, e.g. based on ethnicity (Langer et al., 2017). The attribute of trust covers social trust, that is, the “ability to trust people outside one's familiar or kinship circles” (Mattes &amp; Moreno, 2018, p. 1) and institutional trust, which describes trust in public institutions, such as governments, courts, and the police (Mattes &amp; Moreno, 2018; Rothstein &amp; Uslaner, 2005). Finally, cooperation for the common good refers to the vertical and horizontal collective efforts to promote the welfare of society (Schiefer &amp; van der Noll, 2017). It takes both the form of individual and intergroup cooperation that is directed at the welfare of society at large, and cooperation between citizens and the state, e.g. in the form of civic engagement and political participation (Chan et al., 2006). Such cooperation is not focused on mutual benefit of the groups or individuals involved, but implies the “willingness to subordinate personal needs under the welfare of the social environment” (Schiefer &amp; van der Noll, 2017, p. 589). In that sense, our definition moves beyond the concept of social capital, which is focused largely on the pursuit of common interests (Chan et al., 2006). Although the precise constituents of social cohesion are a matter of debate, the three attributes described above encompass the core elements used in the literature (Chan et al., 2006; Schiefer &amp; van der Noll, 2017). Broader conceptualizations of social cohesion include further attributes, such as equality (Langer et al., 2017), quality of life (Schiefer &amp; van der Noll, 2017), or shared values (Nowack &amp; Schoderer, 2020). We deliberately use a thin concept of social cohesion, as it allows us to analyze these variables as potential explanatory factors, rather than constituting elements of social cohesion (Chan et al., 2006; Schiefer &amp; van der Noll, 2017). 1.2) Research question and objectives While several literature reviews have focused on the conceptualization or measurement of social cohesion (Chan et al., 2006; Fonseca, Lukosch, &amp; Brazier, 2019; Leininger et al., forthcoming; Schiefer &amp; van der Noll, 2017), to date, no attempt has been undertaken to provide a systematic overview of the evidence on determinants of social cohesion. Thus, the aim of this review is to identify and collate the available quantitative evidence on micro-, meso- and macro-level factors that foster or impede social cohesion in Africa. In particular, our review addresses two main questions: (1) Which economic, social, and political factors contribute to social cohesion and its three attributes in African societies? (2) What are the characteristics of, and gaps in, the current literature on determinants of social cohesion in African countries? 2) Methods To address the research questions presented above, we will conduct a scoping review of the literature. Scoping reviews use transparent, systematic, and reproducible methods to search and map the literature on a pre-specified research question (Peters et al., 2020). Our review lends itself to a scoping review methodology as it aims to provide a broad overview of current evidence drawing from a variety of study designs, settings, outcomes, and participant groups (Khalil et al., 2016; Peters et al., 2020). Thus, due to the broad scope and heterogeneous body of evidence, a standardized synthesis of evidence, e.g. in the form of meta-analysis, would not be suitable (Borenstein, Hedges, Higgins, &amp; Rothstein, 2009). The conduct of this review will be guided by the Joanna Briggs Institute (JBI) guidance for scoping reviews (Peters et al., 2020) and the reporting of the review will follow the Preferred Reporting Items for Systematic reviews and Meta-Analyses extension for Scoping Reviews (PRISMA-ScR) checklist (Tricco et al., 2018). 2.1) Pre-registration A protocol of the review will be registered with the Open Science Framework and any changes made during the review process will be reported in the final version of the review (Shea et al., 2017). 2.2) Inclusion and exclusion criteria Types of explanatory factors We define explanatory factors as any variable that fosters or impedes social cohesion and is operating at the micro- or meso-level, e.g. in neighborhoods and cities, or at the macro-level, such as state-wide policies (Bronfenbrenner, 1979). More precisely, we follow Murray et al. (2009) in distinguishing between correlates, predictors, and causes of social cohesion. Correlates are associated with, but need not be causally connected to, social cohesion (Murray, Farrington, &amp; Eisner, 2009). Correlates can be established through observational research designs, e.g. regression analysis. For a factor to be a predictor, it must correlate with, and temporally precede, social cohesion (Shadish, Cook, &amp; Campbell, 2002). This can be demonstrated, for instance, through interrupted time series designs, which compare outcomes before and after the introduction of an explanatory variable (Hudson, Fielding, &amp; Ramsay, 2019). Finally, causes are factors that causally determine levels of social cohesion. Causes are established, for instance, through randomized controlled experiments and quasi-experimental designs with high internal validity (Murray et al., 2009). Types of studies We will include all study designs that offer a quantitative analysis of correlates, predictors, or causes of social cohesion, such as controlled experiments, quasi-experimental studies, e.g. regression discontinuity or interrupted time series design, and other observational studies, e.g. multivariate regression analyses. Qualitative studies, commentaries, letters, and editorials will not be included. Systematic or literature reviews will be excluded but may be used to identify additional relevant studies. Outcomes We will include studies that focus on social cohesion and its three attributes of trust, inclusive identity, and cooperation for the common good (Leininger et al., forthcoming). Social cohesion so defined includes the following indicators, among others: (1) Trust: measures such as self-reported generalized, outgroup/intergroup or political/institutional trust (Chan et al., 2006; Lundmark, Gilljam, &amp; Dahlberg, 2016); observed behavior in trust games (Berg, Dickhaut, &amp; McCabe, 1995) or social dilemma experiments (Fehr, Fischbacher, Rosenbladt, Schupp, &amp; Wagner, 2003) (2) Inclusive identity: measures such as self-reported identification with the nation state relative to identification with the in-group (Langer et al., 2017; Leininger et al., forthcoming) (3) Cooperation for the common good: measures such as membership in associations and voluntary organizations, civic and political participation (Chan et al., 2006), contributions to community public goods (e.g. Blattman, Fiala, &amp; Martinez, 2014) or behavior in public goods experiments (e.g. Fearon, Humphreys, &amp; Weinstein, 2009). As discussed in section 1.1, definitions of social cohesion are far from uniform and some conceptualizations include additional elements, such as equal opportunities, shared values, and quality of life (Schiefer &amp; van der Noll, 2017). For the purpose of this review, these factors will not be viewed as defining elements, but rather as potential explanatory factors of social cohesion. Context and participants This review focuses on social cohesion in African societies. Participants are therefore individuals and/or groups in African countries. No restrictions will be applied regarding participants’ demographic characteristics, such as age, gender, or ethnicity. Studies situated in a geographical area other than Africa will be excluded, e.g. studies focusing on African immigrants in high-income countries. Publication date and language Since this review is the first attempt to systematically search and synthesize evidence on explanatory factors of social cohesion, we will not restrict eligibility based on publication dates of studies. Studies in English, German, and French will be screened for eligibility. 2.3) Search methods for identification of studies As research on social cohesion spans multiple disciplines (Schiefer &amp; van der Noll, 2017), we will search electronic databases with a focus on Social and Political Sciences and Economics (Web of Science), Psychology (PsycInfo), and Public Health (Embase). We will further search the AfricaBib databases, which cover studies with a regional focus on African countries from a variety of disciplines (EPOC, 2013). In order to avoid publication bias, which can systematically distort review findings (Petticrew &amp; Roberts, 2008), we will also hand-search the following websites to retrieve unpublished studies (EPOC, 2013): • Abdul Latif Jameel Poverty Action Lab (J-PAL) database • Innovations for Poverty Action (IPA) database • 3ie evidence portal • Campbell Collaboration evidence repository • World Bank Open Knowledge Repository Table 1 displays the search syntax for Web of Science, which will be adjusted to each database. The search terms were developed based on relevant literature and reviews on social cohesion (Fonseca et al., 2019; Leininger et al., forthcoming; Mattes &amp; Moreno, 2018; Nowack &amp; Schoderer, 2020; Rothstein &amp; Uslaner, 2005; Schiefer &amp; van der Noll, 2017), and an adapted geographic search filter developed by Pienaar et al. (2011). Due to the wide range of study designs included in this review, we did not apply a study methodology filter. Table 1. Search Syntax (Web of Science) #1 Social cohesion overarching TS=(“social cohesion” OR “socially cohesive” OR “social cohesiveness” OR “social capital” OR “social connectedness” OR “social inclusion”) Sources: (Fonseca et al., 2019; Leininger et al., forthcoming; Schiefer &amp; van der Noll, 2017) #2 Trust TS=(“social trust” OR “interpersonal trust” OR “mutual tolerance” OR “particulari$ed trust” OR “generali$ed trust” OR “outgroup trust” OR “out-group trust“ OR “ingroup trust” OR “in-group trust” OR “institutional trust” OR “political trust” OR “trust NEAR/5 institutions” OR “trust NEAR/5 state”) Sources: (Chan et al., 2006; Mattes &amp; Moreno, 2018; Rothstein &amp; Uslaner, 2005; Schiefer &amp; van der Noll, 2017) #3 Inclusive identity TS=(“inclusive identit*” OR “shared identit*” OR “national identit*” OR “state identit*” OR “social identit*” OR “feeling of belonging” OR “sense of belonging”) Sources: (Leininger et al., forthcoming; Schiefer &amp; van der Noll, 2017) #4 Cooperation for the common good TS=((cooperation NEAR/5 “the common good”) OR (responsibility NEAR/5 “the common good”) OR (orientation NEAR/5 “the common good”) OR “solidarity” OR “community cooperation“ OR “civic engagement” OR “voluntary engagement” OR “civic participation” OR “public good$" OR “willingness to cooperate” OR “collective action”) Sources: (Chan et al., 2006; Fonseca et al., 2019; Schiefer &amp; van der Noll, 2017) #5 #1 OR #2 OR #3 OR #4 #6 African countries TS=(Africa OR African OR Algeria OR Angola OR Benin OR Botswana OR “Burkina Faso” OR Burundi OR Cameroon OR “Canary Islands” OR “Cape Verde” OR “Central African Republic” OR Chad OR Comoros OR Congo OR “Democratic Republic of Congo” OR Djibouti OR Egypt OR “Equatorial Guinea” OR Eritrea OR Ethiopia OR Gabon OR Gambia OR Ghana OR Guinea OR “Guinea Bissau” OR “Ivory Coast” OR “Cote d’Ivoire” OR Jamahiriya OR Jamahiryia OR Kenya OR Lesotho OR Liberia OR Libya OR Libia OR Madagascar OR Malawi OR Mali OR Mauritania OR Mauritius OR Mayote OR Morocco OR Mozambique OR Mocambique OR Namibia OR Niger OR Nigeria OR Principe OR Reunion OR Rwanda OR “Sao Tome” OR Senegal OR Seychelles OR “Sierra Leone” OR Somalia OR “South Africa” OR “St Helena” OR Sudan OR Swaziland OR Tanzania OR Togo OR Tunisia OR Uganda OR “Western Sahara” OR Zaire OR Zambia OR Zimbabwe OR “Central Africa” OR “Central African” OR “West Africa” OR “West African” OR “Western Africa” OR “Western African” OR “East Africa” OR “East African” OR “Eastern Africa” OR “Eastern African” OR “North Africa” OR “North African” OR “Northern Africa” OR “Northern African” OR “South African” OR “Southern Africa” OR “Southern African” OR “sub Saharan Africa” OR “sub Saharan African” OR “sub-Saharan Africa” OR “sub-Saharan African” OR “MENA” OR “Middle East and North Africa”) NOT (“guinea pig” OR “guinea pigs” OR “aspergillus niger” OR “African-American$”)) Source: (Pienaar, Grobler, Busgeeth, Eisinga, &amp; Siegfried, 2011) #7 #5 AND #6 Notes: all searches include the Web of Science core collection without limitations on year of publication; TS refers to ‘Topic’, which allows searching title, abstract, and keywords; $ searches for zero or one additional characters, * searches for any group of characters, including no character; all searches include the Web of Science core collection without limitations on year of publication; NEAR/x searches for terms that are within x words of each other 2.4) Study selection and data charting Titles and abstracts of retrieved studies will be screened against the eligibility criteria (Table 2) using the online tool Rayyan (Ouzzani, Hammady, Fedorowicz, &amp; Elmagarmid, 2016). To reduce bias in study selection, a randomly selected 10% of titles/abstracts will be screened by a second reviewer (Stoll et al., 2019). A full-text screening of the remaining articles will be conducted by CW and a randomly selected 10% will be screened by a second reviewer. We will provide an overview of the screening and selection process using the Preferred Reporting Items for Systematic Review and Meta-Analysis (PRISMA)-approach (Moher, Liberati, Tetzlaff, &amp; Altman, 2009). Data charting will be performed by CW. The following study characteristics will be extracted from included studies: • Study type, e.g. experimental, quasi-experimental, observational • Outcome measures used • Country and setting (e.g. rural, urban) • Participant characteristics (age, gender) • Sample size Moreover, we will include information about mediator / moderator analyses, if conducted, as it can provide valuable information about potential mechanisms underlying the relationship between cohesion and its predictor (MacKinnon, 2011). 2.5) Data analysis and presentation Themes emerging from included studies will be presented graphically and the presentation of results will be accompanied by narrative synthesis (Peters et al., 2020). Moreover, we will provide an overview of the characteristics of included studies in tabular form (Tricco et al., 2018). 3) Funding sources This research is funded by the German Federal Ministry for Economic Cooperation and Development. 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