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Abstract

Chinese

As an emergency, food insecurity threatens people’s well-being, while social capital is expected to enhance their resilience in this situation. This study examined the relationship between food insecurity and social capital during the COVID-19 lockdowns in Shanghai. We collected a dataset of 1064 participants by random sampling. Structural equation modelling was used to analyse the dynamics of social capital before and after lockdowns. The results show that the level of post-lockdown social capital was higher than that of pre-lockdown social capital. Pre-lockdown social capital predicted the extent to which people suffered from food insecurity and their approaches to obtaining food. Participation in group purchases and food exchange with other residents predicted the levels of post-lockdown social capital. The results shed light on the interaction between emergencies and social capital. Our study theoretically contributes to understanding social capital through a dynamic perspective.

Keywords

food insecurity lockdowns Shanghai social capital COVID-19 pandemic

Introduction

Since the initial outbreak of the COVID-19 pandemic in Wuhan, China has implemented the stringent zero-COVID policy and claims that this policy is scientific, effective, and standing the test of time. However, its firewall broke down in 2022. After the more transmissible Omicron variant outbreak in China, the infection rates have been drastically soaring since March 2022. Even the state-run media admitted that the scale of infections in the Omicron wave was larger than the previous outbreak in Wuhan (China News Service, 2022). The zero-COVID policy is politically oriented (Zhai, 2023). As the Chinese leadership is determined to unswervingly adhere to the zero-COVID policy, more and more city governments are relying on lockdowns to strengthen the control efforts. As the financial and commercial capital of China, Shanghai also imposed a city-wide lockdown that began with the Pudong district on March 28 and was followed by the Puxi district on April 1. Ordinary people’s livelihood was negatively affected by the lockdowns. Although the food supply was sufficient, Shanghai residents were unable to obtain food (China Daily, 2022; Mannie, 2022). Lockdowns closed groceries and supermarkets, broke the supply chain, and finally made access to food unavailable. Sen (1981) explained famines as failures of people’s entitlement set, and the governments’ wrong and ineffective policies can result in violation of one or all entitlements. Early during the lockdown, food shortage was prevalent in most households in Shanghai, and people suffered from starvation in this affluent city.

Can social capital help people tackle the crisis of food insecurity? Social capital is an important concept in social sciences, which is not only applied to sociology but also in the research fields of economics, political science, and public health (Bourdieu, 1986; Kawachi et al., 1999; Knack and Keefer, 1997; La Due Lake and Huckfeldt, 1998; Putnam, 1995). More and more studies are now paying attention to the role of social capital in disaster risk reduction and recovery (Allen, 2006; Kumari and Frazier, 2021; Partelow, 2021; Sanyal and Routray, 2016; Wu, 2021).

There are two inter-related questions in the studies of social capital in emergencies. First, what role does social capital play in emergencies? It is widely recognised that social capital can promote people’s collective actions. Can social capital facilitate people’s collective actions and cooperation during emergencies? If social capital exerts the same effects in emergencies, people with greater social capital should be more resilient in tough situations. Second, how does social capital change in emergencies? The conventional structural–functionalist perspective is valuable to identify how social capital works in society, but it is static in nature. It is necessary to include a dynamic perspective to examine change in social capital, which is not an invariant constant.

Lockdowns in Shanghai and associated food insecurity problems provided a case to examine the dynamics of social capital in an emergency. The present study employed the dynamic approach to social capital. We established a theoretical framework to analyse the relationship between social capital and emergencies (e.g., food insecurity in this instance). The theoretical model has two layers. First, social capital affects people’s resilience in emergencies by providing access to various resources and enabling collective crisis mitigation efforts. In the case of lockdowns in Shanghai, we examined how the levels of social capital affected the extent to which residents suffered from food insecurity. Second, people’s interactions during emergencies affect social capital that may consequently increase or decrease. The change in social capital is contingent on people’s types of strategies or methods to tackle the crisis such as the pursuit of narrow self-interest or engagement in cooperation in emergencies (Frank, 2003; Knack and Keefer, 1997; Müller et al., 2008). In the case of the lockdowns in Shanghai, we focused on the dynamics of social capital in the situation of food shortage. Residents’ efforts to mitigate the crisis of food insecurity and interpersonal interaction reproduced social capital. An analysis of pre- and post-lockdown social capital in Shanghai helped illuminate the dynamics of social capital in emergencies.

Social capital as an instrument to tackle the crisis

Social capital is a multifaceted concept, which has been defined at collective and individual levels. Coleman (1990: 302) defined social capital as consisting of ‘some aspect of social structure’, which can ‘facilitate certain actions of individuals who are within the structure’. Putnam also viewed social capital as the property of the collective, which constitutes a basis of a vibrant civil society. He defines social capital as the ‘features of social organization such as networks, norms, and social trust that facilitate coordination and cooperation for mutual benefit’ (Putnam, 1995: 67). Community-level social capital is related to social cohesion and has become an important explanatory factor in civic engagement, criminology, and public health studies (Dodd et al., 2015; Kawachi et al., 1999; Takagi et al., 2012). In contrast, other researchers view social capital as individual attributes. Bourdieu (1986: 248) defined social capital as ‘the aggregate of the actual or potential resources that are linked to possession of a durable network of more or less institutionalized relationships of mutual acquaintance and recognition’. Lin (1999: 35) defined social capital as ‘resources embedded in a social structure, which are accessed and/or mobilized in purposive actions’. Even though there is a nuance in the definitions of social capital, most researchers agree that it is derived from social interaction and relationships. Trust, social networks, and norms of reciprocity are key components of social capital (Almedom, 2005; Cook, 2005; Mandarano et al., 2010; Zhai, 2021).

The present study conceptualises and operationalises social capital at the individual level. We defined social capital as social networks in which various resources are embedded, and the factors that maintain the networks, such as trust and norms of reciprocity. This definition is in line with the literature on individual-level social capital (Burt, 1992; Knack and Keefer, 1997; Lin, 2001; Nahapiet and Ghoshal, 1998). Social capital provides valuable resources for people to manage their lives such as getting a job, career development, and maintaining physical and mental health. In emergencies, social capital can help people build resilience to challenges and recover from disasters (Islam and Walkerden, 2014; Paolisso et al., 2019; Wolf et al., 2010). The COVID-19 lockdowns in Shanghai caused a huge disruption in people’s routine social activities, including food shopping. Government rations were limited, and prolonged lockdowns drained residents’ food stocks. As a result, most households faced a crisis of food insecurity. As people were not allowed to go out of their neighbourhood community, their social interaction was primarily confined to the scope of the neighbourhood. The present study focuses on individual-level social capital in a neighbourhood community and examines its relations with food insecurity.

Social capital can bring benefits to people to resist challenges from emergencies. The networks and embedded social resources help reduce risk and contribute to a speedy recovery (Haines et al., 1996; Masud-All-Kamal and Monirul Hassan, 2018; Partelow, 2021). These resources include information, aid, financial resources, and psychological support (Aldrich and Meyer, 2015). In particular, social capital can generate group cohesion and cooperative action among members (Norris et al., 2008; Patterson et al., 2010; Szreter and Woolcock, 2004). It enables people to solve a problem for the common good of the whole community (Sanyal and Routray, 2016). Even in communities with limited protective infrastructure, social capital can help people access resources and aid in emergencies (Adger et al., 2005; Cattell, 2001; Sanyal and Routray, 2016). As people with greater social capital have larger social networks, they can mobilise resources to respond and recover (Sanyal and Routray, 2016).

Urbanisation undermines social integration, local friendship ties, and attachment to neighbourhood communities (Allardyce et al., 2005; Putnam, 1995; Sampson, 1998). Residents in urban communities are from different classes and educational and occupational backgrounds. The characteristic of networks of a neighbourhood community is weak ties that are composed of random encounters. Our study aims to clarify the effect of social capital of neighbourhood communities in emergencies. The strength of weak ties lies in their usefulness in the provision of novel resources (Granovetter, 1973; Wasserman and Faust, 1994). Weak ties can bridge different individuals in a network and facilitate information to diffuse in networks (Cattell, 2001; Watts and Strogatz, 1998). They provide diverse and new ideals, values, and practices into networks, which are particularly vital for resilience (Dowd et al., 2014; Macias and Williams, 2014). During the lockdowns in Shanghai, people with high social capital may have had access to more different resources within the neighbourhood community. These resources included valuable information by which they could obtain food. Therefore, we posited that the levels of social capital are negatively associated with the extent to which Shanghai residents suffered from food insecurity.

H1: Pre-lockdown social capital is negatively associated with the degree of food insecurity.

Natural disasters and other types of emergencies pose threats to the survival of people. Evolutionary models suggest that threat creates a common enemy and elicits group cohesion for threat resolution (Brooks et al., 2021). An emergency can drive human motivation for engagement in cooperation and increase public-good contributions to the ingroup (Francois et al., 2018; Lang et al., 2022; Majolo and Maréchal, 2017). Johnson et al. (2015) found that threat is associated with a greater level of emergency management planning collaboration. As the government announced the lockdown decision abruptly, Shanghai residents did not have sufficient time to prepare adequate food supplies. In addition, the period of the lockdown was prolonged, and most people could not anticipate the government breaking its promises. During the COVID-19 lockdowns in Shanghai, a lack of food motivated people to take measures to mitigate the negative effect of food shortage on their lives. Fairly speaking, the local government distributed food packages to residents, but the quantities were insufficient to prevent a household experiencing hunger. In our survey, 32.15% of the respondents reported that their household had run out of food; 33.08% of respondents reported that they had had to skip a meal because of lack of food; and 47.18% of respondents reported that they ate less because of insufficient food. Shanghai residents had to secure food by themselves. People exchanged food and helped each other. However, food exchange cannot ensure access to adequate food.

Group purchase was another important approach to obtaining food. In Mandarin, group purchase is called tuan-gou. Conventionally, group purchases occurred in personal networks such as family members and friends (Zhang and Tsai, 2015) but the Internet has transformed group purchases so that many participants are unknown to each other. Group purchases have experienced fast growth with the development of e-commerce and gained worldwide popularity. The pattern of group purchases before the COVID-19 pandemic was more spontaneous, with participants living in different places. Consumers endorse preferred products through word of mouth spread electronically, drawing additional buyers to purchase products together (Zhang and Tsai, 2015). As lockdowns restricted trade and commercial activities, costs of food delivery services were high and had to be covered by quantities. Hence, food suppliers required consumers to order a large quantity of food, which was beyond an individual household’s consumption. Residents in the same neighbourhood community banded together to buy bulk quantities of items during the lockdowns. The order was split among them. After the arrival of deliveries, group purchase organisers and volunteers distributed goods to individual households. During the lockdown, group purchases were organised by community and participants were required to live in the same community for convenience in the offline distribution of products. Irrespective of whether they exchanged food with others or engaged in group purchases, their effort of collaboration reflected people’s agency to respond to emergencies. We posited that food insecurity encouraged people to take measures to mitigate the crisis.

H2a. Food insecurity predicts people’s engagement in group purchases in a neighbourhood community.

H2b. Food insecurity predicts people’s food exchange with other residents in a neighbourhood community.

If emergencies can stimulate people’s spontaneous effort to mitigate the negative impact, social capital affects strategies and manoeuvres that people use to tackle a crisis. Social capital provides a lubricant in social interaction and facilitates collective action and social cohesion. Previous studies found that in post-disaster recovery, people with greater social capital tend to participate in cooperative actions and community activities more actively (Kang and Skidmore, 2018; Li and Tan, 2019; Schuller et al., 2000; Wu, 2021). Social capital links a set of networked adaptive capacities of people to deal with challenges and enables them to efficiently resume the rhythms of daily life through cooperation (Aldrich and Meyer, 2015; Makridis and Wu, 2021; Norris et al., 2008; Partelow, 2021).

During the COVID-19 lockdowns in Shanghai, people with high social capital had the advantage of utilising food exchange and group participation to tackle their food insecurity problems. They had broader social networks and could employ their networks to contact others to obtain food. Great levels of social capital also indicate high trust and reciprocity in relationships with others. Networks, trust, and shared norms of reciprocity foster collaboration (Aldrich, 2017; Luthe et al., 2012; Salpeteur et al., 2017). These characteristics of social capital may be weaker in urban communities than in rural areas, but the same functions of social capital do not change in bridging networks. People with greater social capital in neighbourhood communities were less reluctant to interact with other residents. Therefore, we posited that social capital predicted people’s tendency to engage in group purchases and exchange food with other residents. In addition, group purchase of food was normally organised based on a unit of neighbourhood community. Food exchange with others increased interpersonal interactions within the neighbourhood, which produced trust and reciprocity among people. The residents with experience in food exchange may be more willing to participate in group purchases with other residents. We posited that food exchange was positively associated with participation in group purchases.

H3a: Pre-lockdown social capital predicts people’s tendency to engage in group purchases in a neighbourhood community.

H3b: Pre-lockdown social capital predicts people’s tendency to exchange food with other residents in a neighbourhood community.

H3c. Food exchange is positively associated with engagement in group purchases in a neighbourhood community.

Social capital formation as a consequence of crisis mitigation effort

Emergencies reshape people’s living environment and may have an impact on social capital. Several studies indicate the depletion of social capital after emergencies (Miller, 2006; Papanikolaou et al., 2012). However, others found that social capital increases because altruism and collaborative action rise during emergencies (Albrecht, 2018; Chang, 2010; Lee, 2021). In particular, repeated interactions enable people to coordinate activities for mutual benefit and reduce opportunistic behaviour (Cleaver, 2005; Dasgupta, 1988). The dynamic perspective shifts attention from the static structural–functionalist analysis of social capital to the agency of people in the production of social capital. Adversity affects people’s lives and mobilises them to cope with the situation. During emergencies, patterns of social interaction undergo a series of changes, and new values and norms emerge and develop in social relationships. For example, emergencies motivate people to cooperate with others in order to address the crisis, in which their trust and social networks increase, and the norms of reciprocity are established. Social interactions help people foster a new understanding of life and relationships in a neighbourhood community.

Urban communities in Shanghai are loosely organised. People’s social relationships in large cities are more heterogeneous than people in small towns or rural areas (Pelling and High, 2005; Sato and Zenou, 2015). Urban residents have dispersed networks and fewer strong relationships (Fischer, 1982). In most cases, Shanghai residents were unfamiliar with their neighbours and had not even spoken to each other. The COVID-19 lockdowns redefined their living environment. On the one hand, the lockdowns restricted people’s freedoms to go out of their neighbourhood communities, and they were physically disconnected from co-workers or friends. Although people can use communication technology to maintain remote interaction, the role of in-person interaction cannot be completely replaced by remote interaction (Dwyer et al., 2018; Kushlev et al., 2017; Waytz and Gray, 2018). Due to social isolation and disconnection, researchers predict that lockdowns lowered people’s social capital (Pitas and Ehmer, 2020). On the other hand, changes in the environment provided people with opportunities and necessities to have more interaction with other residents in their neighbourhood community. People showed more care and compassion to neighbours who were neglected previously. This type of new interaction may breed social capital. For example, Shanghai residents had to take measures to mitigate the food insecurity crisis. Food exchange and participation in group purchases in the neighbourhood community increased interpersonal interaction. In the process of accessing food, residents got acquainted with each other and built trust and the norms of reciprocity. The crisis-mitigation effort created social capital. Hence, we posited that participation in group purchases and food exchange would generate high levels of social capital during the lockdowns.

H4a. Participation in group purchases in a neighbourhood community increases post-lockdown social capital.

H4b. Food exchange in a neighbourhood community increases post-lockdown social capital.

The reproduction of social capital depends on its previous conditions. Therefore, variations in the change of social capital during the lockdowns may occur among the population. Although people have the agency to create social capital through their interaction with others, the pre-existing level of social capital determines the baseline of changes in social capital. People’s capacity for accessing information and other resources is different, and there is inequality in the distribution of social capital in society (Cleaver, 2005; Cook, 2014; DiMaggio and Garip, 2012; Stanton-Salazar and Dornbusch, 1995). Socially marginalised people have a low level of social capital and are more vulnerable (Cutter and Finch, 2008; Kumari and Frazier, 2021; Morrow, 1999). People with inherently high social capital have a stronger capacity to build trust and networks in interactions during emergencies. As a result, the gap in people’s social capital reproduces itself, and social capital after the emergencies is dependent on the levels of pre-existing social capital.

During the COVID-19 lockdowns in Shanghai, variations in the change of social capital among the population depended on the levels of the residents’ pre-lockdown social capital. People with high social capital tended to engage in more social interactions with other residents to mitigate food insecurity. If cooperation during the lockdowns was beneficial for the production of social capital, people with high pre-lockdown social capital had the advantage of enjoying greater post-lockdown social capital. Hence, we posited that pre-lockdown social capital positively predicts social capital after the lockdowns.

H5. People with greater social capital before lockdowns tended to have a high level of post-lockdown social capital.

The hypotheses formulated above are summarised in Figure 1. It indicates that the pre-existing social capital predicts variations of food insecurity among Shanghai residents and their options of methods to tackle the crisis. Meanwhile, food insecurity promotes people’s effort to mitigate the negative impact of the crisis, which may facilitate the production of social capital in people’s interactions. This framework shows the dynamics of social capital in an emergency.

Figure 1.

The theoretical model of relationships between the variables.

Methods

Participants

Because of the lockdowns and social distancing policy, we conducted our survey via the internet in Shanghai from 21 April to 6 May 2022. We used the stratified random sampling method to select potential respondents. The sampling plan defines the quota of age group, gender, and education to increase the representativeness of the samples. Based on the quota of demographic factors and registered information of potential respondents, the survey agency randomly selected their mobile phone numbers and sent an invitation to participate in the survey. If they agreed to participate in the survey, clicking the link in the message would direct them to the survey page. Then, we explained the research purposes and obtained their informed consent before the survey. The identities of the respondents were kept anonymous, and they had the right to quit answering the questionnaire at any point during the survey. The respondents received monetary compensation for their time and participation. In the end we obtained 1064 samples. The age of the respondents ranged from 18 to 79. There were 543 (51%) male and 521 (49%) female respondents. Further information detailing the respondents’ education and marital status is displayed in Table 1.

Table 1.

Descriptive statistics.

	Mean	SD	Min	Max
Food insecurity	4.324	1.407	1	7
Pre-lockdown social capital	4.478	0.966	1.5	6.5
Post-lockdown social capital	4.794	1.017	1.25	6.5
Group purchases	5.086	1.811	1	7
Food exchange	3.991	2.000	1	7
Age	37.867	11.143	18	79
Gender (female = 1)	0.490	0.500	0	1
Education	4.352	1.004	1	5
Marital status (married = 1)	0.784	0.412	0	1
Parental status (with child(ren) = 1)	0.585	0.493	0	1

Measures

Food insecurity was measured using a scale developed by Cafiero et al. (2018), which gauges the ability of households and individuals to access food. As our research theme is food insecurity caused by the COVID-19 lockdowns, rather than a lack of money, we adapted the scale to the context of the lockdown in Shanghai and asked respondents to evaluate how the lockdowns influenced their food consumption. Examples of the items include, ‘At some point during Shanghai’s lockdowns, your household ran out of food’; ‘At some point during Shanghai’s lockdowns, you ate less than you thought you should because of insufficient food’; and ‘At some point during Shanghai’s lockdowns, you had to skip a meal because of lack of food’. Responses were rated on a seven-point Likert scale (1 = strongly disagree to 7 = strongly agree). Higher scores on the measure indicated greater food insecurity levels. Cronbach’s alpha of the scale in the current study was 0.853.

According to the theoretical framework, we measured people’s pre- and post-lockdown social capital, respectively. People were unable to go out of their neighbourhood communities during the lockdowns, and their social interaction primarily occurred in neighbourhood communities; thus, we focused on their social capital in such communities. Operationalisation of social capital follows previous studies, using the following three indicators: trust, social networks, and norms of reciprocity (Putnam, 1995; Subramanian et al., 2001; Takagi et al., 2012). Pre-lockdown social capital was operationalised by social trust (‘Before the lockdowns, in general, most people in the neighbourhood community can be trusted’), social networks (‘Before the lockdowns, on average, how many people do you have contact with in the neighbourhood community in a typical week day?’), and reciprocity (‘When you have a difficult problem to manage, you can obtain help from people in the neighbourhood community before the lockdowns’ and ‘If someone helps you, you would also help other people in the neighbourhood community before the lockdowns’). Responses to social networks were rated on a five-point Likert scale (1 = none to 5 = more than 10 people). Responses to social trust and reciprocity were rated on a seven-point Likert scale (1 = strongly disagree to 7 = strongly agree). These items were composited to create an index of pre-lockdown social capital, with Cronbach’s alpha of 0.694.

Post-lockdown social capital was measured using a similar method. It was operationalised by social trust (‘In general, most people in the neighbourhood community can be trusted after the lifting of the lockdown’), social networks (‘How many people can you have contact with in the neighbourhood community in a typical weekday after the lifting of the lockdown?’), and reciprocity (‘When you have a difficult problem to manage after the lifting of the lockdown, you can obtain help from people in the neighbourhood community’ and ‘If someone helps you, you would also help other people in the neighbourhood community after the lifting of the lockdown’). Responses to social networks were rated on a five-point Likert scale (1 = none to 5 = more than 10 people). Responses to social trust and reciprocity were rated on a seven-point Likert scale (1 = strongly disagree to 7 = strongly agree). These items were composited to create an index of post-lockdown social capital, with Cronbach’s alpha of 0.736.

During the lockdowns in Shanghai, two primary methods for accessing food through interpersonal interaction in the neighbourhood community were examined. Food exchange was measured by asking respondents how often they accessed food through exchange with other residents in the neighbourhood community. Group purchase was measured by asking respondents how often they accessed food through participating in group purchases. Responses were respectively rated on a seven-point Likert scale (1 = never to 7 = every day).

Social-demographic variables were controlled for in the analysis. Age was calculated based on the difference between the year of survey and the year of a respondent’s birth. Gender was a dummy variable in which ‘male’ took a value of 0 and ‘female’ took a value of 1. Education was an ordinal variable with six categories: 1 = ‘elementary school’, 2 = ‘middle school’, 3 = ‘high school’, 4 = ‘vocational college’, 5 = ‘university and above’. The marital status was measured by a dummy variable (‘other’ = 0, ‘married’ = 1). The parental status was a dummy variable and was coded based on whether respondents had child(ren) or not (‘without child(ren)’ = 0, ‘with child(ren)’ = 1).

The descriptive characteristics of the sample are presented in Table 1.

Analytical strategy

Structural equation modelling (SEM) was used to identify the relations among pre-lockdown social capital, food insecurity, and post-lockdown social capital during the COVID-19 lockdowns in Shanghai. As SEM can accommodate both direct and mediating effects in an integrated model with clearly specified path analysis, it is an appropriate modelling strategy to effectively test the complex relationship among independent, mediating, and outcome variables. Therefore, we adopted this modelling strategy for empirical analysis.

Results

The dynamics of social capital during the lockdowns

We first compared the difference between pre- and post-lockdown social capital in neighbourhood communities (see Figure 2). The same person’s social capital, before and after the lockdowns, was measured respectively, and we analysed the difference by a paired samples t-test. The results show a significant difference between pre-lockdown social capital (M = 4.478, SE = 0.030, CI [4.420, 4.536]) and post-lockdown social capital (M = 4.794, SE = 0.031, CI [4.733, 4.855]), d = 0.316, SE = 0.032, CI [0.253, 0.379]. On average, Shanghai residents’ social capital after lockdowns was higher than the previous level (t (1063) = 9.775, p < 0.001). The results indicate that Shanghai residents’ social capital in the neighbourhood community increased after the lockdowns.

Figure 2.

The difference of pre-and-post social capital, p < 0.001.

The relationships between food insecurity and social capital

We estimated coefficients following the theoretical model presented previously in Figure 1. The fit indices were as follows: RMSEA = 0.051, GFI = 0.999, and CFI = 0.995 with χ²/df = 3.731. This model had an acceptable fit. Table 2 displays the results of the structural equation model for the dynamics of social capital and its relations with food insecurity during the COVID-19 lockdowns in Shanghai.

Table 2.

Path coefficients for the structural equation model.

Paths			95% CI
Paths	b	SE	Lower	Upper	p
Direct effects
Pre-lockdown SC → Food insecurity	−0.065	0.032	−0.131	−0.006	0.033
Pre-lockdown SC → Group purchases	0.057	0.032	−0.004	0.119	0.045
Pre-lockdown SC → Food exchange	0.106	0.031	0.045	0.167	0.000
Pre-lockdown SC → Post-lockdown SC	0.419	0.030	0.358	0.475	0.000
Food insecurity → Group purchases	0.164	0.032	0.097	0.226	0.000
Food insecurity → Food exchange	0.340	0.030	0.281	0.398	0.000
Group purchases → Post-lockdown SC	0.163	0.032	0.099	0.227	0.000
Food exchange → Post-lockdown SC	0.058	0.032	−0.005	0.121	0.046
Food exchange → Group purchases	0.304	0.033	0.237	0.365	0.000
Indirect effects
Pre-lockdown SC → Post-lockdown SC	0.017	0.007	0.004	0.032	0.011
Pre-lockdown SC → Group purchases	0.015	0.013	−0.011	0.041	0.254
Pre-lockdown SC → Food exchange	−0.022	0.011	−0.045	−0.002	0.036
Food insecurity → Post-lockdown SC	0.063	0.011	0.041	0.086	0.000
Food insecurity → Group purchases	0.103	0.014	0.077	0.132	0.000
Food exchange → Post-lockdown SC	0.050	0.011	0.030	0.074	0.000

Note: χ²/df = 3.731, GFI = 0.999, CFI = 0.995, RMSEA = 0.051.

RMSEA, (root mean square error of approximation); GFI, (comparative fit index); GFI, (goodness-of-fit index); CI, confidence interval; SE, standard error; SC, social capital.

Pre-lockdown social capital negatively predicted the extent to which people suffered from food insecurity during the COVID-19 lockdowns in Shanghai, b = −0.065, 95% CI = [−0.131, −0.006], p = 0.033. Food insecurity positively predicted people’s tendencies to participate in group purchase and exchange food with others, b = 0.164, 95% CI = [0.097, 0.226], p < 0.001; b = 0.340, 95% CI = [0.281, 0.398], p < 0.001. Pre-lockdown social capital also positively predicted people’s tendencies to participate in group purchase and exchange food with others, b = 0.057, 95% CI = [−0.004, 0.119], p = 0.045; b = 0.106, 95% CI = [0.045, 0.167], p < 0.001. In addition, food exchange with others positively predicted people’s participation in group purchase, b = 0.304, 95% CI = [0.237, 0.365], p < 0.001.

There may be mediation effects of food insecurity on the relationships of pre-lockdown social capital and the approaches to accessing food. Therefore, we conducted the bootstrap estimation to examine the indirect associations between pre-lockdown social capital and the methods for accessing food during the food insecurity crisis. The indirect path from pre-lockdown social capital to group purchase was insignificant, b = 0.015, 95% CI = [−0.011, 0.041], p = 0.254. However, the indirect path from pre-lockdown social capital to food exchange with others was significant, b = −0.022, 95% CI = [−0.045, −0.002], p = 0.036. The results reveal that there was a mediation effect of food insecurity on the relationship between pre-lockdown social capital and the food exchange.

The food insecurity crisis and the residents’ subsequent effort to mitigate it had an impact on changes in social capital. Participation in group purchases and food exchange with others positively predicted people’s post-lockdown social capital, b = 0.163, 95% CI = [0.099, 0.227], p < 0.001; b = 0.058, 95% CI = [−0.005, 0.121], p = 0.046. The levels of pre-lockdown social capital also positively predicted people’s post-lockdown social capital, b = 0.419, 95% CI = [0.358, 0.475], p < 0.001. There may be multiple mediation effects on post-lockdown social capital. Therefore, we conducted the bootstrap estimation to examine the indirect associations between several variables and post-lockdown social capital. The indirect path from pre-lockdown social capital to post-lockdown social capital was significant, b = 0.017, 95% CI = [0.004, 0.032.], p = 0.011. The indirect path from food exchange to post-lockdown social capital was significant, b = 0.050, 95% CI = [0.030, 0.074], p < 0.001 The indirect path from food insecurity to post-lockdown social capital was also significant, b = 0.063, 95% CI = [0.041, 0.086], p < 0.001.

Robustness tests

We changed path directions or deleted some paths to test whether the results were valid, and compared the results with those of the final model presented in Table 2. First, assuming that the two methods for accessing food were independent, we removed the paths from food exchange to group purchase. The estimates and significance level of test statistics were consistent with the final model, but the model did not have a good fit. The fit indices were as follows: RMSEA = 0.217, GFI = 0.965, and CFI = 0.831 with χ²/df = 50.850. Second, assuming that social capital in a neighbourhood community did not predict the residents’ cooperation, we removed the paths from pre-lockdown social capital to group purchase and food exchange. The estimates and significance level of test statistics were consistent with the final model, but this model did not have a good fit either. The fit indices were as follows: RMSEA = 0.076, GFI = 0.992 and CFI = 0.969 with χ²/df = 7.067. Third, assuming that pre-lockdown social capital did not directly predict food insecurity but had an indirect effect on the degree of food insecurity through people’s crisis mitigation efforts such as food exchange and participation in group purchases, we removed the path from pre-lockdown social capital to food insecurity and reversed the path direction from food exchange and group purchase to food insecurity. However, this model did not have a good fit. The fit indices were as follows: RMSEA = 0.081, GFI = 0.994, and CFI = 0.976 with χ²/df = 8.050. Therefore, the estimates of the final model remained robust with different variable selection methods, and the final model had a better goodness of fit than other alternative models.

Discussion and conclusion

This study examined the dynamics of social capital and its relations with food insecurity in the COVID-19 lockdowns of Shanghai. The results of the present study indicate that pre-lockdown social capital predicted the level of food insecurity and people’s methods for accessing food during the COVID-19 lockdown in Shanghai. People scoring high social capital tended to suffer less from the problems of food insecurity. This is probably because people with high social capital had more resources to mitigate their own crisis of food insecurity. Our study shows that social capital in urban communities facilitated cooperative action in emergencies. People with high social capital were more likely to participate in group purchases and exchange food with other residents. Although the direct paths from pre-lockdown social capital to the two methods for accessing food were significant, the significant level of the relationship between pre-lockdown social capital and food exchange was higher. Compared to participation in group purchases, food exchange with other residents needed more social interaction and people with greater social capital had the advantage in obtaining food through this method. In addition, food exchange helped increase interpersonal interaction and facilitated residents to get involved in group purchases with others. With regard to the mediation effect, as people with greater social capital were less likely to suffer from food insecurity, these people also had a low need to access food through exchanges with others.

The conventional structural–functionalist perspective focuses on the constructs and function of social capital, and its primary research interest is on what social capital can bring about. This perspective is very important because it defines the core components of social capital and its functions, such as working as the basis of a vibrant civil society or resources to get a job. However, this static perspective neglects the dynamics of social capital that is not an invariant constant. In particular, emergencies magnify the variability of social capital. During the COVID-19 lockdowns, difficulties in accessing adequate food resulted in an emergency, which provided a case to examine the dynamics of social capital. As people were forced to stay at home or were not permitted to go out of the neighbourhood community, we focused on the dynamics of their social capital in such a community. This study examined the relationship between social capital and food insecurity, including how pre-lockdown social capital was related to the extent to which people suffered from food insecurity and how the effort to mitigate the crisis of a lack of food was related to post-lockdown social capital.

The effort to mitigate the food insecurity crisis predicted post-lockdown social capital. The dynamic perspective paid attention to the changes in social capital rather than viewing it as an invariant constant. Interactions in emergencies may bolster the pursuit of narrow self-interest or encourage cooperation (Frank, 2003; Knack and Keefer, 1997; Müller et al., 2008). When crisis mitigation cannot be achieved by individual effort and cooperation between group members is indispensable, this context facilitates cooperation. Although urban communities do not have strong interpersonal interaction, an emergency alters the environment and disrupts the daily routine. People have the agency to build trust, networks, and norms of reciprocity through interactions in the community, in which social capital forms. Cox and Perry (2011) argue that the living space, where material and symbolic markers of individual and collective identities are grounded, is disrupted in emergencies; consequently, people engage in a process of reorientation and reshape their identities. Bourdieu (1986) stresses the importance of membership in a social group in the development of social capital. Emergencies motivate people to rethink and redefine their neighbourhood communities, and interaction with other residents wakens and intensifies their awareness of memberships of a community. In our case, food insecurity facilitated people’s actions to mitigate its negative effects. Amid the lockdowns, Shanghai residents participated in group purchases, exchanged food with other people, and helped each other. These behaviours increased interpersonal interactions with other residents in the neighbourhood community, even those they had not met before. Interactions bred trust, broadened networks, and intensified adherence to the norms of reciprocity. As a result, people’s social capital increased during the COVID-19 lockdowns in Shanghai.

The change in social capital was surely not independent of its previous level. People with greater levels of social capital before the lockdowns also had the advantage of achieving higher social capital in the post-lockdown stage. Cleaver’s (2005) study shows that the chronically poor cannot effectively exercise agency. The positive effect of engagement in community activities or cooperative action varies among people, and the constraints of exercise of agency result in the reproduction of unequal social capital. Individual motives are not a major cause for such outcomes. Sociologists explained that people’s incapability to exercise agency was due to their socially situated position (Granovetter, 1990; Long, 1990, 2001). People with low pre-lockdown social capital were situated in a disadvantaged position, which restrained the extent to which they could develop their post-lockdown social capital. Their potential capacities were systematically frustrated by inequitable social and economic structures and the deeply-seated norms and values in their minds (Cleaver, 2005; Han and Zhai, 2022). Therefore, a reconsideration of the relationship between the agency of building social capital in emergencies and potential constraints and inequality in reproducing social capital is relevant to understanding the dynamics of social capital. The results of the present study indicate a possibility of a gap persisting in social capital among people after emergencies. As social capital is linked to various life outcomes, such as health status, academic performance, employment opportunities, and occupational attainment, it can generate enormous inequality in society (Cleaver, 2005; Cook, 2014; DiMaggio and Garip, 2012; Stanton-Salazar and Dornbusch, 1995). The persistent gap in social capital among people and its reproduction in the dynamic of social capital indicate that an enduring inequality remains among people.

Footnotes

Declaration of conflicting interests

The author(s) declared no potential conflicts of interest with respect to the research,authorship,and/or publication of this article.

Funding

The author(s) disclosed receipt of the following financial support for the research,authorship,and/or publication of this article: This study was supported by the National Natural Science Foundation of China (72274121),the Humanities and Social Science Fund of Ministry of Education of China (20YJC630033),the Philosophy and Social Sciences Foundation of Shanghai (2019BGL022),and the National Social Science Foundation of China (21BGL219).

ORCID iD

Yida Zhai

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The association between food insecurity and social capital under the lockdowns in COVID-hit Shanghai

Abstract

Keywords

Introduction

Social capital as an instrument to tackle the crisis

Social capital formation as a consequence of crisis mitigation effort

Methods

Participants

Measures

Analytical strategy

Results

The dynamics of social capital during the lockdowns

The relationships between food insecurity and social capital

Robustness tests

Discussion and conclusion

Footnotes

Declaration of conflicting interests

Funding

ORCID iD

References