Abstract
This article analyses how employers and university-educated jobseekers behave when networks are overly used, and connections supersede merit in recruitment. It advances the debate by exploring the effects of networks on how the labour market for the university-educated functions, and how the normalisation of network-based recruitment affects this segment of the labour market. Using data from Sierra Leone, findings show that overuse of networks for recruitment can be harmful to the labour market, and is reminiscent of Schelling’s model where individual incentives lead to a collective result that is less desirable. Actions by firms promote perceptions of unfairness in the labour market. Jobseekers search based on the perceived probability of being recruited due to network membership, and not on the most compatible or desired job. The data show that some unconnected workers respond by limiting search, exiting the labour market, becoming underemployed, or attempting to build networks.
Introduction
The labour market is both a market and social institution. Acemoglu and Robinson (2012) define market institutions as the rules of the game concerning the organisation of economic transactions. Social institutions emerge when there are practices and learned behaviours, which reproduce within and across groups and organisations, becoming institutionalised (Berger and Luckmann, 1966). In the case of the labour market, these groups are workers and employers, and organisations are firms. Understanding how individual labour market agents behave is thus important as this affects the labour collectively.
This article focuses on the labour market as an institution and explores how the use of networks in recruitment decisions affects the ‘rules of the game’, and thus the functioning of the labour market. Throughout the article, the term ‘network-based recruitment’ refers to the use of social, ethnic and political networks in the hiring decision. Social networks, as discussed here, are formed around friends, family and members of the same community; ethnic networks form around common ethnicities; and political networks around political affiliation. An important feature of network-based recruitment explored in this article is where connections through networks can supersede external signals of merit in hiring decisions. Connections can be between potential candidates and employers, current employees or those external to the firm but within the employer’s network. The labour market, as used here, refers to where employers (on the demand-side of the labour market) and jobseeker/workers (on the supply-side) meet and interact for the purpose of hiring. The analysis draws on data for the university-educated, and as such, the findings primarily relate to the labour market for this group, though some findings may be generalisable to other segments of the labour market.
Previous research has considered networks in obtaining employment, largely focusing on the benefits. Networks provide information on candidates and reduce information asymmetries, which can improve matching between workers and firms in the labour market (Abel et al., 2020; Calvó-Armengol and Jackson, 2004; Montgomery, 1991). They are also a cost-effective way to search (Ioannides and Loury, 2004). From the lens of social capital, social networks provide benefits to those within these structures (Coleman, 1988), and can be good for human development and firm performance (Christoforou, 2010; Lofthouse and Storr, 2021). However, there can be a dark side to social capital (Portes, 2000), as social networks may spawn ‘antisocial capital’ or negative effects (Streeten, 2002). In the labour market, networks can lead to an insider-outsider situation where outsiders are disadvantaged (Calvó-Armengol and Jackson, 2004). If networks form around certain sociodemographic characteristics such as gender, ethnicity or immigration status, these groups may be disadvantaged in employment outcomes (Arbex et al., 2019; Beaman et al., 2018; Behtoui and Neergaard, 2010; Brown et al., 2015; Clarke and Smith, 2024; Harris and Ogbonna, 2016; Hudson et al., 2017; Lalanne and Seabright, 2022; Montgomery, 1991; Tassier and Menczer, 2008).
This article builds on this literature. Not only does it explore differences in outcomes of connected versus unconnected jobseekers, as in previous studies; but it also advances the debate by analysing how network-based recruitment shapes the functioning of the labour market as an institution, and how employers and jobseekers respond to the normalisation of network-based recruitment. Central to the analysis is the sociological concept of homophily, or that contact between similar people is more likely (McPherson et al., 2001). The article first asks: how does the existence of networks affect decision-making and behaviour in the labour market? Subsequently, it assesses if the observed outcome from networked-based recruitment is optimal for the labour market. Methodologically, qualitative data from Sierra Leone, a developing country, are analysed. Data are from 46 interviews with employers/policymakers and 19 focus group discussions with 83 university-educated workers/jobseekers. The data are analysed using thematic analysis, and subsequently discussed using the theoretical framing of externalities from the economics literature.
The data reveal two related findings. First, recruiters and jobseekers often rely heavily on networks when searching for employment/employees, and that the use (and overuse) of networks for recruitment can be harmful to the labour market as this affects perceptions of fairness by those looking for jobs. Second, in Sierra Leone, networks are often used at the intensive margins – that is, being employed because of membership of a network or group. In this case, network use surpasses an information-sharing function and facilitates hiring based on nepotism. Jobseekers thus adopt a probabilistic approach to decision-making, where job applications are not based on the most compatible or desired job, but on the job with a higher perceived probability of success based on networks. Alongside this, jobseekers attempt to gain access to networks and ‘become connected’ to those deemed to have access to networks that can influence recruitment. Over time, some jobseekers settle for less desirable but attainable jobs (underemployment), reduce their active job search, or become discouraged and exit the labour market altogether. Collectively, these results can be characterised as a negative externality where the actions of connected firms and jobseekers (and those attempting to become connected) impose costs on other agents in the labour market, rendering the overall functioning of the labour market less than socially desirable.
Although observed and reported individual behaviour reveal individual clustering to various groups and deliberate efforts to become connected and use networks, respondents widely regard the use of such connections as disadvantageous and harmful to the labour market collectively. As Schelling (1971: 144) notes, ‘the active choice is more like congregation than segregation’ and differs from models of discrimination in the labour market (Arrow, 1971; Becker, 1957). Schelling (1971) demonstrates that even mild individual preferences for similar neighbours can lead to highly segregated neighbourhoods. As is argued in this article, the same can occur in the labour market. As agents form and utilise networks based on homophily, an insider-outsider situation results, with connected insiders and unconnected outsiders. In the absence of self-correction, the outcome is a labour market, or parts thereof, which function around networks; or as Rojas (2006) notes, the market and network can become indistinguishable. The findings offer an explanation to one piece of the puzzle where we observe low levels of search by firms and jobseekers in the presence of an expanding skilled workforce in countries like Sierra Leone (Statistics Sierra Leone [SSL], 2015: 36). Beyond this, there are lessons for other developing and developed countries, as network-based recruitment has been shown to span income level and continents (Topa, 2011).
The rest of this article is organised as follows. The next section reviews various literature which position groups/networks in the labour market. The case study on Sierra Leone is presented next, together with the data collection and analytical methods. Evidence of networks and hiring in the Sierra Leonean labour market are then provided, followed by a section that discusses the collective labour market effects. The final section concludes the article.
Groups, networks and the labour market
Homophily is the principle that contact between similar people is more likely than dissimilar groups; that is, ‘similarity breeds connection’ (McPherson et al., 2001: 415). Lazarsfeld and Merton (1954) distinguish between two types of homophily: status homophily and value homophily. Status homophily is based on some ascribed status, and can derive from inherited sociodemographic characteristics like race, ethnicity, sex or age, and acquired characteristics like religion, education or occupation. On the other hand, value homophily is based on values, attitudes and beliefs. This is not to say that the two are entirely mutually exclusive. As McPherson et al. (2001) note, values often derive from social positions/status. In addition to this, values and beliefs may lead to clustering around organisational foci (e.g. certain schools or types of employment). Importantly, group clustering linked to both types of homophily can translate into differences in networks and network distances, which as Lin (2001) notes, can ultimately impact social structures. In cases where homophily results in social structures which create differential opportunity structures and some individuals have differential access based on their social relations, there can be significant consequences for social inequality and the reproduction of inequalities (Bottero, 2007; Lin, 2001). Inequality in the labour market is one manifestation.
Networks and social capital
Networks are common in the labour market. Vacchiano (2022) uses network and social capital theory to categorise contacts in the job-search process into informers (who provide information), employers (who hire) and influencers (who influence recruitment decisions). A review of the literature suggests that using networks for job search spans continents and national income levels (Topa, 2011). This is underscored by the willingness of contacts to help jobseekers, especially those they perceive as ‘good’ workers (O’Connor, 2013). In the US, for example, estimates range from 50% to 87% of people securing employment through networks (Topa, 2011: 1199–1200). In the Sierra Leone case study, 62.8% of the labour force found their job through a family or friend (SSL, 2015: 26). This practice is common in both rural and urban Sierra Leone, and declines as education increases (SSL, 2015).
Networks offer preliminary screening and help to ameliorate information asymmetries on ability levels and match quality (Montgomery, 1991). They also engender trust, which can enhance the predictability of behaviour – leading to improved worker fit, enhanced workplace culture, the formation of social bonds, higher worker and team productivity and worker retention (Choi and Storr, 2021). For jobseekers, networks can provide information about vacancies and reduce search costs (Ioannides and Loury, 2004). Such gains from networks can be seen as a benefit of social capital – the potential resources linked to a network or social structure (Bourdieu, 1986; Lin, 2001). Coleman (1988) notes that social capital facilitates productive activity and is itself productive, asserting its public goods features. It can facilitate information flows, social mobility and community organisation (Granovetter, 1973). Even ‘weak ties’ – seen as casual relationships or small-scale interactions – can be beneficial (Granovetter, 1983). More recent literature show that social capital can be good for human development and firm performance (Christoforou, 2010; Lofthouse and Storr, 2021).
On the other hand, Portes (2000) warns of the less desirable side of social capital, emphasising some of its spurious benefits. Streeten (2002) similarly notes that social networks may spawn ‘antisocial capital’ – a collective term used by the author to describe the negative effects of social capital, such as corruption, nepotism, cronyism, social exclusion and impediments to economic progression. Networks may also compromise wider public interest, particularly when lobbying and rent-seeking become a feature of these structures (Chamlee-Wright and Storr, 2011).
In the labour market, homophily and its related network-based job search and matching can lead to an insider-outsider situation (Calvó-Armengol and Jackson, 2004). Arbex et al. (2019) build a theoretical model of networked-search and show that networked workers are privileged in the labour market as they are more likely to obtain referrals, find jobs, have longer tenured contracts and earn higher wages. Empirical studies have largely confirmed Arbex et al.’s (2019) theoretical predictions. Using firm-level data on job applications, Brown et al. (2015) show that employee referrals are associated with an increased probability of being interviewed and hired, an initial starting salary premium that gradually disappears and longer tenure at the company. As O’Connor (2013) aptly concludes, social networks can be a productive job-search strategy, but not all workers benefit equally.
When networks are formed around gender, ethnicity or race, some minority groups may be disadvantaged in employment outcomes (Montgomery, 1991; Tassier and Menczer, 2008). For example, experimental evidence shows that women are statistically less likely to be referred by men (Beaman et al., 2018). Lalanne and Seabright (2022) also highlight gender differences in networks and career progression, noting the importance of ‘the old boys club’. Ethnic minorities can also be disadvantaged in the labour market due to (lack of) access to some networks, which manifest in gatekeeping in an organisational context (Harris and Ogbonna, 2016). They also earn less or hold fewer senior roles (Clarke and Smith, 2024; Hudson et al., 2017). Importantly, these disadvantages in employment outcomes by ethnicity affect those from the shop floor (Hudson et al., 2017) to senior levels (Clarke and Smith, 2024). Alongside ethnicity, membership of a stigmatised immigrant group has been associated with being embedded in social networks that limit acquisition of valuable social resources or being excluded from social networks with valuable resources, which in turn is correlated with earning less (Behtoui and Neergaard, 2010). Beyond this, when networks are vital for social and employment support, spells of unemployment render some disadvantaged groups more at risk of long-term unemployment (Bolibar et al., 2019), thus creating a vicious cycle.
Labour market discrimination
These findings emphasise that the labour market is fundamentally different to markets for goods and services. In the goods market, the implicit theoretical assumption is one of impersonal exchange where there is no relationship between buyer and seller. This simple assumption breaks down in cases where there are direct personal relationships between employer and employee, or between employees (Arrow, 1998). For example, the pioneering theoretical contribution of Becker (1957) attached disutility (dissatisfaction) to White individuals who entered into contracts with Black individuals in America. Arrow (1971) built on Becker’s work, analysing two forms of taste-based discrimination: employer–employee and employee–employee discrimination. Taste-based discrimination is non-market oriented, and contrary to profit maximisation of the firm. Themes around taste-based discrimination particularly emerge in the literature related to different employment outcomes by race/ethnicity (Harris and Ogbonna, 2016; Hudson et al., 2017) and gender (Lalanne and Seabright, 2022). It has been argued that the market can correct for taste-based discrimination, particularly wage effects, given the associated economic costs (Williams, 2011). However, given the wealth of empirical evidence showing differences in labour market outcomes by race over time (see Lang and Lehmann (2012) for an overview), such self-correction arguably has not taken place.
Statistical discrimination, another form of discrimination, is consistent with profit maximisation and was proposed as a way of reducing information asymmetries. For instance, if group A is on average less productive than group B because of some unobservable characteristic, over time the employer uses the observable trait of group A as a proxy for the unobservable trait that is correlated with lower productivity (Phelps, 1972). Though the theory of statistical discrimination provides a market-based explanation of discrimination, distinguishing this from non-market explanations like taste-based discrimination is difficult as this requires the ability to observe labour’s marginal product (Arrow, 1998).
Importantly, like network-based recruitment, statistical discrimination can lead to clustering of some groups in particular occupations, and different labour market outcomes across groups. Arrow (1998: 97) acknowledges the similar effects of social networks and forms of labour market discrimination, stating that ‘social linkages alter resource allocation processes’. Arrow notes that although social networks are initially developed for non-economic reasons, there is still an impact on economic efficiency. Importantly, Arrow argues that the value placed on maintaining social interactions, or one’s place in the network, can overcome profit maximisation (Arrow, 1998). This in turn can lead to discriminatory practices which contradict economic interests like firm profit.
Political networks, corruption and the labour market
In developing countries, the ills of using networks (mainly political networks) in the labour market intersect with the literature on corruption. Szeftel (2000: 427) defines corruption as ‘the misuse of public office, public resources or public responsibility for private – personal or group – gain’. The use of office to promote or directly influence the career advancement of a candidate either for personal, political or social gain can therefore be seen as an example of corruption in the labour market. The mechanisms driving these interactions range from pure exchange to political identity/networks or coercion (Szeftel, 2000), and can manifest in ‘jobs for the boys’, among other things (van de Walle, 2012: 13).
Fafchamps and Labonne (2017) show that relatives of current office holders in the Philippines are more likely to be employed in better-paying jobs. At the macro-level, higher perceptions of corruption increase youth unemployment, especially among the educated (Bouzid, 2016). Bouzid (2016) argues that this then reinforces a vicious cycle of using corruption to find jobs. Corruption can also reduce labour force participation and the employment to population ratio (Cooray and Dzhumashev, 2018). At the firm level, corruption can limit employment and stifle firm growth (Beltrán, 2016).
In sum, the evidence that networks in the labour market unequally harm some groups is therefore strong. The findings from this study corroborate this established finding. Further to this, the article contributes to the literature by showing that network-based hiring fundamentally affects the functioning of the labour market as agents report behaving differently to how they would in the absence of network-based recruitment.
Case study, data and analytical methods
Sierra Leone is a developing country in West Africa with a population of just over eight million (World Bank, 2024). The economy is largely agrarian, though the services sector has been blossoming (World Bank, 2024). The Sierra Leonean labour market is characteristic of that of many developing countries, with high levels of informality (up to 90% of the labour force), fewer wage jobs available for those who want them and formal employment that is largely in the public sector (40% of formal employment) (SSL, 2015). There is also significant underemployment, which particularly affects youths, men, tertiary degree holders and those residing in the capital, Freetown (SSL, 2015). In Sierra Leone, jobs are advertised in both print and online media, and applications are primarily made in person (i.e. hard copies are physically submitted to organisations). Despite these formal channels, 62.8% of the labour force found their job through a family member or friend (SSL, 2015: 26). Importantly for the present study, the labour force survey tells a story of: (i) an increasingly skilled workforce, but low levels of job search using formal channels; (ii) a high unemployment rate among skilled workers; and (iii) initial evidence of limited job search (SSL, 2015).
To further unpack this story and better understand search experiences in the labour market, data from interviews and focus group discussions are used here. For both interviews and focus groups, a maximum variation sampling approach was used (Patton, 2002), where the sample was purposefully selected to allow a broad range of perspectives based on differing positionalities and labour market experiences. Participants were accessed using a mix of snowballing and cold calling. The total number of interviews and focus groups were determined when ‘saturation’ was achieved; that is, additional sampling generated little/no new insights (Guest et al., 2006). Full details of participants, and interview and focus group guides, have been published in Harris (2020: 266–274).
Interviews were used to understand the demand-side of the labour market and were conducted with 46 employers, government officials and third-sector representatives working on labour market issues across 39 organisations. The organisations included five public sector organisations, 15 formal private sector companies, 14 third-sector organisations (such as NGOs and civil society groups) and five categorised as other (representing universities, workers’ unions and employer groups). A key informant from the Ministry of Labour facilitated introductions to some respondents, and these respondents in turn suggested other participants who were contacted. To broaden reach outside of these networks, organisations were ‘cold called’ using a list obtained from the Ministry of Trade. Interviews were held with either the managing director, human resources (HR) manager or the person responsible for labour/employment issues in the case of the third sector. Interviews lasted up to one hour and were conducted in English at the interviewee’s office.
Focus group discussions were used to understand the supply side of the labour market: 19 focus group discussions were conducted with 83 university-educated workers/jobseekers. Of this 83, 29 were graduates just about to enter the labour market, 24 were in-between jobs and 30 were employed at the time of data collection – thus, offering a range of employment experiences and limiting bias to any one employment status. Focus group participants graduated from a range of study programmes in the arts, social sciences, natural sciences and engineering, they include members who identify from all the 15 main ethnic groups in Sierra Leone, and male and female voices are almost equally represented. The 29 graduates who were just about to enter the labour market were contacted and invited based on their participation in a previous survey of university students (see Harris (2023) for details of the survey; 36 were contacted and 29 participated). The other 54 respondents were contacted based on a list provided by the National Youth Commission (which offers entrepreneurship/business support and runs a national internship programme) or were referred by other participants. Focus groups ranged from four to six participants and lasted between one to two hours. Focus groups took place at Fourah Bay College and the office of a small IT company that was made available to the research team. Discussions were led by the main researcher, with assistance from a local research assistant. English was the primary language used, with Krio (the local language) being occasionally spoken.
Thematic analysis formed the main analytical method. This entailed identifying, examining and extracting dominant themes from the qualitative interviews and focus group discussions (Guest et al., 2011). First, notes from both the lead researcher and research assistant (from the focus group discussions) were combined in Microsoft Word. Three primary themes/codes were identified which mapped onto the research areas of interest. These included: (i) how do networks manifest in the labour market for university-educated workers in Sierra Leone (code: ‘network type’); (ii) what are the effects of networks in the labour market (code: ‘network effects’); and (iii) how do employers and jobseekers behave because of networks in the labour market (code: ‘employer/jobseeker response’). Thereafter, an interpretative case-by-case approach was used to further analyse the data, and secondary codes identified based on reading of the data. Table 1 illustrates the relationship between the primary and secondary codes. All coding and data extraction were done in Microsoft Excel.
Coding framework.
Homophily and networks in the labour market: Evidence from Sierra Leone
Manifestations of homophily and networks in the Sierra Leonean labour market
Based on the qualitative data, homophily in the Sierra Leonean labour market can be largely classified as status homophily, using the terminology of Lazarsfeld and Merton (1954). It stems from political, ethnic, social and organisational networks. The labour force survey only captured data on the share of the labour market finding employment through social networks (family and friends) (SSL, 2015). Therefore, the analysis here takes a deeper dive into other types of networks in the labour market. Each type of homophily manifests differently, with varying effects.
From the data, political networks were the most pervasive (Table 2). They are deep-rooted in Sierra Leone’s history and trace back to colonial times (Harris, 2014). Political networks in the labour market manifested when the recruiter actively sought out a political connection (in cases of public sector hiring) or used political influence to affect the hiring process (in a private company or NGO). Using political influence outside of the public sector was often part of a
Network types in the Sierra Leonean labour market.
Ethnic networks were the second most prevalent in the data. In Sierra Leone, this largely mirrors – and was often mentioned alongside – political networks, as the two dominant political parties are divided along ethnic lines: the All People’s Congress (APC) affiliated with Temnes and the Sierra Leone People’s Party (SLPP) affiliated with Mendes. Kandeh (1992) discusses this ‘politicisation of ethnic identities’, and Casey (2015) provides empirical evidence that ethnicity is a strong predictor of political loyalty. Alongside Temnes and Mendes, there are at least 15 other ethnic groups (SSL, 2015), but these groups historically align with one of the two dominant parties (Casey, 2015). A candidate’s ethnicity can be easily deduced from their surname, which is present on all application documents. Jobseekers in one of the focus group discussions mused about the power of a Krio surname. Krios are descendants of former enslaved persons who were returned from the Americas and Caribbean, and often do not have ‘traditionally’ African surnames (like Williams or Roberts, for example).
The data revealed that social connections form around friends, family and members of the local community. Sierra Leoneans take pride in what was colloquially referred to as ‘
And finally, organisational networks form around secondary schools and universities. Some HR managers reported drawing on connections from their alma maters and asking the respective heads of department to suggest capable candidates. Some lecturers who were also employed in industry created internships for students at their workplace, which often resulted in full-time salaried employment. The stated rationale was that having been exposed to a particular pedagogical system, the employer is able to ascertain, to some extent, the types of knowledge and skills that a graduate from that institution would attain. This strategy minimises information asymmetries on ability in line with previous literature (Abel et al., 2020; Calvó-Armengol and Jackson, 2004; Montgomery, 1991).
Importantly, unlike in previous literature (Abel et al., 2020; Calvó-Armengol and Jackson, 2004; Montgomery, 1991), except networks from secondary schools and universities, the types of homophily apparent in the data are independent of ability and thus have little potential to reduce asymmetric information in this regard. This suggests that hiring in Sierra Leone, in many instances (but not all), reduces to who the candidate knows, rather than what they know.
The effects of network-based recruitment on job creation, job search and matching
Table 3 presents extracts from focus group discussions. Mohammed’s, Alpha’s and Aminata’s stories were not unique. These three examples illustrate that the use of networks in the labour market spans gender, area of study, types of organisations applied to and employment status, and they underscore perceptions of unfairness in the labour market which reportedly stem from excessive use of networks. Like Mohammed, many respondents shared accounts of being side-lined for candidates who were thought to be less qualified but better connected. ‘Connections’, ‘connectivity’, ‘nepotism’, ‘favouritism’ and ‘interference’ were terms used to describe what jobseekers and the employed perceived as one of the biggest challenges in the labour market. Colloquially, ‘
Network-based recruitment in the labour market – the labour supply perspective.
According to respondents, connections affect both the creation and allocation of jobs. Respondents noted that fewer jobs are created as some existing workers negotiate employment beyond the official retirement age, resurfacing as experts on fixed-term contracts. This limited progression up the organogram and prevented the creation of new entry-level vacancies. Regarding job allocation, a dominant theme in the data (as shown in Mohammed’s and Alpha’s stories) was that meritocracy can at times be second to networks in the selection process. This perception was common among those entering the labour market, the unemployed searching for work and those permanently employed.
Employers reported advertising fewer jobs and a culture of mistrust among jobseekers was evident in the focus groups. Jobseekers complained that some positions were not advertised, and that positions advertised ‘were not for them’. Many respondents believed that advertisements were merely a ‘formality’ and candidates had already been ‘cherry-picked’ from the employer’s network or as a favour to someone else. As such, most respondents who were just about to enter the job market anticipated high spells of unemployment and estimated waiting at least a year before finding full-time work. Those searching for at least a year already (some up to 10 years) had all but given up hope of finding permanent employment and either turned to involuntary self-employment, teaching, volunteering in the formal sector (hoping to be absorbed) and/or relying on friends and family for financial support. For those employed, some had waited up to seven years before securing permanent employment, with only a few securing work soon after graduating.
Lack of confidence in the recruitment process on the labour supply side is not without reason. On the demand side, many HR managers reported facing ‘external pressures’ on hiring decisions, ‘having their hands tied’ to the situation and ‘adhering to instructions from above’. Some reported responding to hiring requests (and minimising reprisals from not recruiting suggested candidates) by not advertising certain positions externally, absorbing current/former interns and volunteers, referring to their pool of CVs or asking for personal recommendations. Table 4 provides quotes from employers in the public, private and NGO sectors and illustrates that influence from political networks on recruitment decisions occurs across all sectors and was perceived to be increasing over time.
Network-based recruitment in the labour market – the labour demand perspective.
Of the 30 employed focus group participants, 13 reported knowing someone working at the hiring organisation when they applied. Three respondents reported not formally applying but were gainfully employed. All three were connected to someone at the hiring organisation – two were interns and were interviewed when a vacancy arose and the third was informed by a friend employed at the organisation to attend an interview. On average, those unconnected at the time of applying made more applications since graduating (14.7 vs 4.3 applications) and faced a longer average waiting time between applying and securing their current employment (6.1 vs 2.8 months) see Table 5.
Search experience of employed respondents.
Behavioural responses to network-based recruitment in the labour market
Firms and workers responded by either embracing or refuting network-based recruitment. These findings are new as behavioural responses to network-based recruitment in the labour market have not been previously studied. On the demand side, there are two types of firms that emerged from the data: non-compliers and compliers. Non-compliers used their contacts to advertise vacancies and placed value on referrals from trusted sources in their network. Non-compliers thus derived informational benefits from networks as in previous literature (Abel et al., 2020; Calvó-Armengol and Jackson, 2004; Montgomery, 1991). Compliers also gained informational benefits from networks (like non-compliers), but this group further embraced networks and, importantly, would hire a suggested candidate over another candidate that may be of equal or higher ability – for example, HR manager 2 from Table 4. These firms complied with a culture of nepotism. They derived benefits from network-based recruitment and were perceived as ‘unfair recruiters’ by jobseekers. Importantly, some recruiters engaged in network-based recruitment because of external (often political) pressures which drove them away from open advertisements, although these employers reported the desire to recruit based on merit – for example, HR manager 1 and the NGO director from Table 4. This behaviour overcomes issues of external interference to some extent but limits the pool of eligible candidates and, importantly, projects an image of unfairness to observing jobseekers. Together, this affects how the labour market functions.
On the labour supply side, there are two types of workers: the connected and unconnected. Connected workers utilised networks to gain employment. Unconnected workers responded in various ways (Figure 1 and Table 6). First, the unconnected limited search intensity and reduced the number of applications submitted. Owing to distrust in the system, applicants could not tell which jobs were legitimately open until they reached the final stages (as in Mohammed’s case), so many reported updating expectations and not applying to avoid wasting time and resources. This triggers a discouraged worker effect for some unconnected workers. From the data, those who had been searching for at least a year had all but given up hope of finding desirable formal employment and relied on working in the informal sector ‘like hairdressing and food selling, like shawarma’, engaging in short-term casual work or teaching (Table 6). As discussed below, limited job search from these types of workers reduces the candidate pool for companies trying to recruit fairly, imposing a cost on these firms. Second, some unconnected jobseekers altered their labour supply decision as they observed data over time and made a judgement as to which firms, or groups of firms, were fair and which were not. An example of this strategy in Sierra Leone was applying to jobs at donor organisations, international NGOs and foreign-owned/managed organisations that were perceived to be fairer by nature of their international status. Third, applicants attempted to become connected, thereby increasing the likelihood of successful future employment.
The (unconnected) jobseeker response.
Labour market response of unconnected jobseekers.
The labour market outcome: A collective result
In economics, an externality is characterised by a mismatch between the private costs/benefits borne by/accrued to an individual or firm from an activity and the social costs/benefits of that activity (Ayres and Kneese, 1969). Externalities arise when the costs/benefits to those not directly involved in a transaction (third-party costs) are not fully considered. If private costs are lower than social costs, this results in a negative externality and if social benefits exceed private benefits, the result is a positive externality. A classic example of a negative externality is the smoker who is unlikely to consider the costs of second-hand smoke when purchasing cigarettes.
More formally, Figure 2 illustrates a negative externality. As shown, the marginal social costs (
Negative externality.
When the net benefits of network-based recruitment remain high, the behavioural responses discussed above lead to firms and jobseekers increasingly utilising networks for recruitment. Though such behaviour may be rational at the individual level, the increasing role of networks imposes a negative externality on some agents in the labour market.
First, in attempting to develop and use connections, jobseekers do not consider the social costs of their actions, but merely personal costs and benefits. This is different to a general equilibrium analysis of the labour market where there is a price-effect – for instance, an increase in the supply of lawyers drives down the wages of all lawyers. In the present case, the actions of connected jobseekers and those trying to become connected do not affect the wages of the unconnected for a given job (beyond changes expected in a normal labour market). Instead, the chances of getting a job are affected (and consequently earning a wage altogether). Again, this is beyond the normal probabilistic changes when there are large numbers of graduates to few jobs. The added dimension here is that some applicants are shifted to the ‘back of the queue’ or assigned a lower probability of being selected – a particularly burdensome effect in the context of job scarcity. The effect is reinforced if the relative share of networked highly qualified/skilled workers increases, which further encourages firms to recruit from within their networks. This is a cost to unconnected jobseekers on the labour supply side, who then have a lower probability of exiting a state of unemployment.
Second, if some jobseekers only apply to perceived fair firms or sectors, the candidate pool for firms attempting to openly recruit but operating in a sector deemed to be unfair will be lower. This is a cost to some fair firms on the labour demand side.
Third, unconnected jobseekers may be pushed out of the labour market. This changes the relative share of highly qualified/skilled workers in the potential pool of recruits. If a large share of highly qualified/skilled workers is discouraged and exits the labour market, some firms lose out as the candidate pool is less qualified/skilled on average. This in turn affects the output potential and performance of the firm. For jobseekers, those that exit are unable to reap the full rewards of investing in education and training. This is a cost to both firms and jobseekers, thus affecting both the demand and supply sides of the labour market.
By utilising networks, marginal social costs are higher than marginal private costs for those using networks. The wedge between marginal social and private costs arises as: (i) information is distorted (via non-credible advertising); (ii) there is reduced search and lower wages from unemployment/underemployment of unconnected outsiders; (iii) discouraged workers exit the labour market limiting the pool of candidates available to fair firms; (iv) there is sub-optimal matching between workers and firms as connected lower-qualified/skilled jobseekers may be recruited; and (v) some jobseekers match based on the perceived probability of success (and fairness) rather than intrinsic preferences. This is characteristic of a negative externality, and the overuse of networks beyond a socially optimum level (Figure 2). The data align with this result as respondents generally believe that everyone would be better off if there were less nepotism and network-based recruitment in the labour market. The outcome is reminiscent of Schelling’s (1971) model where individual incentives lead to a collective result that is less desirable to society as a whole.
The proposition here is not that the use of networks is altogether harmful, but that the excessive use of networks is highly likely to be, as this affects perceptions of fairness in the labour market by those trying to find work. The literature has established that information asymmetries about worker type may lead to gains from using networks in recruitment (Abel et al., 2020; Ioannides and Loury, 2004; Montgomery, 1991). Networks also foster trust, which can be good for team dynamics and productivity (Choi and Storr, 2021). Importantly, the effects on the labour market depend on whether networks are used to share information only, or also to circumvent merit-based recruitment and hire based on connections, as the latter affects perceptions and perceptions in turn affect behaviour.
Conclusions
The main contribution of this article has been to explore and deepen understanding of the perceptions of and behavioural responses to network-based recruitment in the labour market. The data from Sierra Leone give evidence of network-based recruitment in the labour market for university-educated workers formed around homophily (political, ethnic and social networks). Although using networks to find jobs/workers can be rational and advantageous at the individual/firm level, when networks are overused and associated with a perception of unfairness in the labour market, this can lead to an outcome that is less than socially desirable.
The article has not tried to quantify the share of firms/workers in this segment of the labour market who use connections to secure employment – this remains an area for further study. Moreover, given that the findings are based on data from university-educated jobseekers and employers recruiting these types of workers, further research should expand on this to include lower-skilled workers to better understand if/how networks affect their labour market experiences.
What the article has shown using the data analysed is that when some firms hire based on network membership and when there is the belief among jobseekers that such behaviour exists, this matters for decision-making and behaviour in the labour market. In such a situation, it is expected and observed, that jobseekers apply heuristics and probability judgements when deciding to apply for jobs. Some firms may not advertise vacancies, while others may advertise as a formality – polluting the information set. This can result in lower levels of job search. Connections, networks and homophily thus create a market friction and prevent smooth interactions between jobseekers and recruiters. This distortion is likely more impactful in a country like Sierra Leone where the formal economy, and consequently formal employment, is limited.
From the data, all use of networks is not harmful. Some organisations reported using networks to advertise vacancies, and some jobseekers learnt of vacancies from networks. Networks become problematic when a candidate is hired
The outcome is reminiscent of Schelling’s model where individual incentives lead to a collective result that is less desirable to society as a whole. In the famous segregation model, Schelling (1971) demonstrates that even mild preferences at the individual level for similar neighbours can lead to highly segregated neighbourhoods. This body of literature recognises that in cases of complex systems, individual behaviour can have striking effects on collective results, even though such effects may be unintended by, or even undesirable to the individual.
It may be possible for the market to correct itself if hiring from networks becomes exceedingly costly, causing those who use network-based recruitment to confront the costs of their choices. In the absence of self-correction, the market may continue to become more networked. In such a case, human capital choices can be distorted, which can lead to productivity and output being lower than potential. This is particularly costly in developing contexts like Sierra Leone where the formal economy is already small. There are also likely distributional implications if some jobseekers are excluded from the labour market. For example, if being connected is correlated with other sociodemographic measures, such as income, gender or urban versus rural place of birth, then connections in the labour market would serve to reinforce existing social inequalities. In this case, the outcome is both inefficient and unequitable.
Although the behaviours of employers and jobseekers have been analysed here, the findings speak to broader policy at the national level. From the data, the ecosystem is such that it is in employers’ and jobseekers’ interests to use networks in the way they do. The behaviours of employers and jobseekers are grounded in the social culture of the labour market, which is what policy must target. Policies to tackle the ills of networks in developing countries have often been framed in the corruption literature. The findings here demonstrate that networks can also be burdensome to the labour market, and, as such, policies should look beyond the public purse to social and economic institutions like the labour market when addressing the ills of networks. This can include measures like mandating all vacancies to be publicly advertised and requiring hiring processes to be subject to internal and external audit. It is only in addressing these issues that a free and fair labour market can exist.
Footnotes
Acknowledgements
This work would not have been possible without financial support from the International Growth Centre (IGC) – project number 39408; my Sierra Leone team of Abass Kargbo, Mousa Sesay, Sidi Saccoh, Umaro Tarawalie and Alpha Jalloh; and my research participants who gave me their time. I am also grateful for comments and feedback from Christopher Adam, Andy McKay, Martin Williams and Alex Jones. And to the taxpayers of Trinidad and Tobago, who funded my doctoral studies.
Ethics statement
The research received ethical approval from the Oxford Department of International Development Departmental Ethics Review Committee – Ref. No. CUREC 1A/ODID C1A 17-031.
Funding
The author disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This study was funded by the International Growth Centre (grant number 39408).
