Post-COVID-19 Analysis of Fiscal Support Interventions on Health Regulations and Socioeconomic Dimensions

Abstract

The coronavirus (COVID-19) pandemic has profoundly affected public health and socio-economic structures globally. This research conducted a post-COVID-19 analysis of the role of fiscal support interventions on COVID-19 health regulations such as mandatory non-pharmaceutical interventions like face masks, social distancing, periodic lockdowns which include restrictions on movement, and socio-economic dimensions. This quantitative research obtained 302 responses from different households in the Eastern Cape, Gauteng, Kwa-Zulu Natal, and Limpopo Provinces in South Africa. The results reveal that the relief fund (R350 unemployment grant, unemployment insurance fund claim, and food parcel distribution, among others) mediated the relationship between COVID-19 health regulations and poverty levels and the relationship between COVID-19 health regulations and health and well-being. The relief fund also mediated the relationship between COVID-19 health regulations and employment levels. Support packages from the R500 billion government support, which included loan guarantees, job support, tax and payment deferrals and holidays, social grants, wage guarantees, health interventions, and municipalities support, moderate the relationship between COVID-19 health regulations and the family and social support. These results validate the impact of the fiscal support intervention by the government in mitigating its emergency intervention with COVID-19 health regulations. This strengthens the theory of intervention, highlighting that multiple dynamics make interventions complex as shown by mediation and moderation results. Furthermore, this study highlights intervention being central to the management of the crisis. The study highlights the importance of comprehensive intervention for future preparedness, thus advancing the crisis–intervention perspective. Advances in these areas are critical to mitigate the impact of the next pandemic or similar major events in society. This can be achieved through improved pandemic timely response with effective economic stimulus, social relief, strong legal framework, and anti-corruption policies.

1. Introduction

The coronavirus (COVID-19) pandemic caused a devastating effect and crisis in societies across the globe [1,2,3,4]. The World Health Organization [5] reported about 465 million cases of COVID-19 infections, almost 6 million deaths, and 365 million recoveries. South Africa has reported over 4 million COVID-19 infections [6], with approximately 102,000 COVID-19-related deaths, reflecting a significant impact of the pandemic on the nation [7]. In addition to the negative health impact, the COVID-19 pandemic also caused economic distress [8,9], exacerbated the plight of vulnerable populations [10], and compounded food security issues and instability of informal sectors [11]. The International Labour Organization [12] noted that an estimated 255 million full-time jobs vanished globally due to the COVID-19 pandemic. Additionally, it negatively impacted the lifestyles, well-being, and community support structures [13,14]. In South Africa, the pandemic led to significant job losses and a contracting GDP [15], businesses faced revenue losses, restructuring, and closures, prompting adaptations such as digitalization and remote work arrangements, and school attendance dropped by 48.2% after shutdown, with disparities influenced by socioeconomic factors such as race, gender, and geographical location [16]. Furthermore, the lockdown exacerbated domestic violence, with increased reports linked to economic dependency and social norms [17].

In May 2023, the World Health Organization [18] advised that despite the persistently high level of global risk assessment, there was a decrease in risks to human health, primarily attributable to the prevalence of population-level immunity resulting from prior infections. As such, the COVID-19 pandemic no longer meets the criteria for a public health emergency of international concern (PHEIC). Despite this, researchers caution that the incidence of zoonotic diseases, which are transmissible from animals to humans, and others such as pandemic influenza are increasing, with the likelihood of a novel pandemic outbreak elevated beyond historical levels [19,20,21]. Aligned with this potential upcoming challenge, the Director of the World Health Organization advised that “in the face of overlapping and converging crises, pandemics are far from the only threat we face, when the next pandemic comes knocking—and it will—we must be ready to answer decisively, collectively, and equitably” [22].

The pandemic’s harmful consequences necessitated that governments assume an interventionist role to alleviate the high social, health, and economic impacts of the COVID-19 lockdown on sustainable development [23,24]. Despite this, most of the studies on the socioeconomic impact of COVID-19 focused on the macro levels [24,25,26], with very few using the lenses of the affected communities. The few that focused on individuals in communities zoomed in on the health aspects of COVID-19 [27,28], on practitioners [29,30,31,32], or educational or work tools [33,34,35]. Furthermore, most studies on the impact of the COVID-19 pandemic on socioeconomic studies mainly focused on one or two socio-economic dimensions and not more comprehensive multiple dimensions [15,16,17,36]. For example, there are studies that focused on the impact of COVID-19 on well-being only [37,38,39], income and expenditure with financial inflows only [40], poverty and food insecurity only [41], and income and livelihood [42] and poverty and health disparities [36]. This study analyzed holistic socioeconomic dimensions, which include employment, poverty, life quality, health and well-being, and family and social support. More importantly, this study examines multiple socioeconomic dimensions at the community level. This helps to advance targeted interventions by understanding socioeconomic factors and allows for tailored support to vulnerable populations [43]. It also helps to have detailed data-driven approaches that can utilize socioeconomic data to improve predictive modeling for pandemic trajectories and their influence at community levels, thus promoting timely responses [44].

As such, this study is crucial to enhance the knowledge to draw some lessons from and improve the future readiness of the countries, especially the developing countries, in major disruptions such as pandemics or other major events. Fauci et al. [21] (2023, p.1) highlight the importance of lessons highlighting, amongst others, “Early, rapid, and aggressive action is critical in implementing public health interventions and countermeasure development”. As such, the overarching question of this research is: How has the government’s fiscal support intervention impacted the effects of health regulations on socioeconomic dimensions in South Africa during the COVID-19 pandemic? The rest of the paper is comprised of the following sections. It starts with the theoretical foundation that underpins the study, followed by hypothesis development. This is followed by the methodology applied, the results, and then a discussion. The paper closes by providing conclusions which include implications, limitations, and suggestions for future research.

2. Literature Review and Hypotheses Development

2.1. Crisis and Intervention Theories

A crisis can have multiple effects on society, with COVID-19 regarded as a crisis that caused health devastations and economic distress [25,45,46]. The emergence of different outcomes during a crisis is contingent upon the characteristics of the system experiencing the crisis and its interactions with other systems. Applying the crisis theory to COVID-19 entails analyzing its definition, societal implications, impact, and the intricate systems involved [47]. Weiss [48] explained that after an issue has been recognized—crisis, a desired outcome specified, and the stakeholders whose actions will determine the outcome have been named, policymakers must determine what the government may do to encourage the actors to alter their actions. An interventionist’s strategy for exerting influence requires the specification of who should intervene, the target of whom should be influenced, the mechanism of how to intervene, and when and where a concrete social intervention should occur [49,50]. A wide variety of governmental interventions in a society can range from policy, programs, directives, guidelines, and by-laws [51,52]. Koehler and Chopra [53] explained that the extent and scope of interventions should be considered in light of the specific nature of the state. This can include direct and indirect interventions which can range from strong prescriptive interventions to weak interventions involving guiding, facilitating, and encouraging approaches [54,55].

2.2. COVID-19 as Health Crisis

Governments around the world invoked emergency regulations to curb the spread of the COVID-19 pandemic [56,57] through controlling the disease and flattening the curve [58] using multiple non-pharmaceutical interventions [59,60]. This resulted in measures such as nationwide lockdowns, travel limitations and curfews, border closures, and mandatory wearing of masks among other actions [61,62]. Other interventions included nation-wide testing and tracing of exposure to quarantine and minimizing the spread of the virus. To ensure compliance, some countries like South Africa deployed military forces to support the police in enforcing the mandated lockdown [63]. The COVID-19 pandemic also resulted in increased mental health concerns, which resulted from extreme levels of grief and loss, extended periods of isolation, unresolved pain, and fatigue [64], on-going anxiety and depression from lost sources of income as well as COVID-19 inducing chronic health concern. Ferhani and Rushton [61] posit that governments worldwide have progressively recognized pandemics as not only public health concerns but also as dangers to national security. As such, several countries integrated disease risks and responses into their national security policies, resulting in security policy communities assuming significant and novel responsibilities in the country’s preparations for and management of pandemics. The COVID-19 pandemic disproportionately affected communities because the virus is spreading more rapidly in densely populated areas where there is limited capacity to implement effective measures to control its spread.

2.3. COVID-19 as Socioeconomic Crisis

There are a multitude of socioeconomic dimensions that were affected by the COVID-19 pandemic and its subsequent emergency health regulations in South Africa and other countries around the world. These include poverty levels, employment levels, life quality, health and well-being, and family and social support. Quaglia and Verdun [65] argued that the COVID-19 pandemic commenced as a public health crisis in early 2020, precipitated quick and severe economic repercussions, resulting in the most significant economic recession since World War II, with a global contraction of 3.5 percent in 2020. The regions most affected by the pandemic-induced rise in poverty are South Asia and sub-Saharan Africa [66]. However, its impact is particularly devastating for sub-Saharan Africa, while also having a catastrophic effect on emerging nations as a whole. The poverty issues were worsened by the prevailing high levels of unemployment [67]. Perry [68] emphasized the interdependency of poverty, unemployment, and inequality. The unemployment and earnings inequality data in South Africa have regressed over the years to remain at exceptionally elevated levels compared to international standards [69,70]. Ajefu et al. [71] also noted that low- and middle-income countries faced substantial difficulties accessing essential necessities like food and medicine during the COVID-19 pandemic compared to high-income and industrialized nations. The restricted availability of fundamental provisions, such as sustenance, water, and other essential necessities, generally obstructed the general welfare of the populace. Despite this, countries like South Africa provide free water and electricity for certain households [72] and free primary health care [73]. In addition, this situation placed millions of South African inhabitants in a state of helplessness, causing them to feel isolated and despondent. Families were prohibited from visiting one another and were kept to their respective residences, resulting in minimal social interaction [74]. Citizens were left requiring human intimacy more than ever before as the health and well-being of the world population were gravely affected, with families separated from loved ones who were infected with the virus, loved ones who died alone in hospitals [75] when visitation rights had to be suspended, and families who had to bury their loved ones in isolation and without the support from their extended families and the circle of friends. It has also proven challenging to maintain a healthy lifestyle with all the unpredictability and worry over finances, elderly parents, job security, lifestyles, and mental health [76]. The outbreak of the COVID-19 pandemic eroded some of the family and social support due to multiple factors, including travel restrictions and limitations on the number of attendees at funerals. The bereaved families were compelled to independently organize the burial and interment of their deceased relatives without the presence of family and community elders. Communal grieving is a prevalent custom in numerous African countries, persisting until the funeral ceremony. In many nations, the grieving procedure involves various therapeutic traditions, some of which necessitate performing them in the presence of the deceased’s body at their residence. However, this was not feasible because of the lockdown time [77].

Despite the aforementioned, most governments, including South Africa, did not have adequate foresight to treat the COVID-19 pandemic crisis as an integrated health, social, and economic crisis, initially spending a lot of effort treating COVID-19 as only a health crisis. Boin and Rhinard [78] (p. 655) acknowledge that despite the European Union’s performance on handling COVID-19 being positive, it “acted quickly after a somewhat slow start and was very effective in mobilizing a variety of resources”. In South Africa, the government announced an economic intervention—fiscal support interventions toward the end of April 2020, a month after the first lockdown. Even after that, the main focus of the advisory bodies was limited to the health imperatives of COVID-19, mainly driven by the Department of Health with less to negligible focus on economic impact [79].

2.4. Fiscal Support Interventions

Governments around the world intervene with fiscal support to help minimize the impact of COVID-19 on the economy and its citizens. Globally, governments were clearly committed to safeguarding their citizens and businesses in the face of what is expected to be the most significant economic downturn [80]. Siddik [81] analyzed the economic stimulus for the COVID-19 pandemic across more than 100 countries and categorized the countries as low, medium, and high response groups to COVID-19. Country features such as median age, hospital beds per capita, total cases of the COVID-19 pandemic, GDP, health expenditure, and the COVID-19 government stringency index were significant determinants of economic stimulus. Nations with a comparatively larger informal sector prior to the epidemic had implemented a lower fiscal policy package [82,83]. The South African government also implemented the Economic Relief Package to alleviate the dire hardship experienced by many individuals [83,84]. The economic interventions have encompassed a wide range of strategies and magnitudes, varying from 2.5% to a reported 50% of the Gross Domestic Product [85]. The Institute for Economic Justice (IEJ [86] contended that the magnitude of the rescue package should align with the extent of the crisis. Typically, the amount of COVID-19 government spending announced globally has been approximately equivalent to the projected economic decline in each country. This is because, in the context of a lockdown, each unit of currency spent is expected to have a diminished effect on stimulating economic activity compared to normal circumstances. Estimates of the economic loss in South Africa were approximately 10% before the announcement of the R500bn (USD = 27.03 bn), representing about 10% of the GDP. This intervention included loan guarantees, job support, tax and payment deferrals and holidays, social grants, wage guarantees, health interventions, and municipalities support [87]. These interventions were in line with other countries around the world [85,88,89]. The government’s actions in many countries globally have yielded both favorable and unfavorable consequences on the economy and its uncertainty. This underscores the significant implications of government intervention in the economy amidst the COVID-19 pandemic.

2.5. Conceptual Model and Study Hypotheses

Central to the theory of crisis is the concern about the dichotomy between reality and social construction, as well as their potential interplay, of which the reality is evident with COVID-19 with the level of health crisis [5], increased poverty [90], and large-scale job losses. Crises like the COVID-19 pandemic necessitate prompt action from leaders [91]. The emergency health regulation intervention to safeguard lives had a domino effect resulting in negative socioeconomic impacts [23,92] propelling governments to also implement economic interventions. The complexity of the COVID-19 pandemic thus brings forth the need to understand both the crisis and intervention. The study’s conceptual model illustrates the influence of the COVID-19 Health regulations on socioeconomic attributes and the moderating effect of the fiscal support interventions (Figure 1). The study analyzes the COVID-19 health regulations (COV) as the independent variables denoted with X and focuses on five socioeconomic dimensions (SED): poverty levels (PVL), employment levels (EMP), life quality (LQT), health and well-being (HWB), and family and social support (FML) as dependent variables denoted with Y. The direct relationship model for the ith subject is:

$$Y_i={\alpha }_0+{\beta }_1{X}_i+{\epsilon }_i$$

(1)

where ${\alpha }_0$ is the constant, ${\beta }_1$ is the coefficient, and $\epsilon$ is the error term. The first hypothesis is:

Hypothesis 1 (H1).

COVID-19 has a negative effect on socioeconomic dimensions (1a to 1e).

In this mediation analysis, we considered the intervening construct—relief fund (RLF) denoted with Z, which is known as the mediator to help explain how COV influences SED, which comprises PVL, EMP, LQT, HWB, and FML, denoted $X_1$, $X_2$ …$X_5$, respectively. The mediation model for the ith subject is:

$$Z_i={\alpha }_0+{\beta }_{xz}{X}_i+{\epsilon }_{zi}$$

(2)

$$Y_i=Y_0+Y_{zy}{Z}_i+Y_{xy}{X}_i{+\epsilon }_{yi}$$

(3)

${\epsilon }_{zi}$ and ${\epsilon }_{yi}$ are assumed to be uncorrelated error terms. In the analysis, the direct effect moves from X to Y while controlling for the mediator, Z. As such, $Y_{xy}$ is the direct effect, while the indirect effect X to Y through the mediator, Z represented by the product of ${\beta }_{xz}$ and $Y_{zy}$, and the total effect being the sum of direct and indirect effects of X on Y. The second hypothesis is:

Hypothesis 2 (H2).

Relief fund interventions mediate the COVID-19 health regulations andsocioeconomic dimensions (2a to 2e).

The study also has an interest in the moderation effect of the financial support package (SUP). Hair et al. [93] posit that moderation is a scenario where the relationship between two constructs is not constant but rather varies based on the values of the moderator (SUP), which can change the strength or even the direction of the relationship of X and Y. Considering Equation (1) as the basic model, we introduce the moderator, W, into the model and the equation is:

$$Y_i={\alpha }_0+{\beta }_1{X}_i+{\beta }_2{W}_i+{\beta }_3\left(X_i\times W_i\right)+{\epsilon }_i$$

(4)

where ${\beta }_2$ is the effect of the moderator on Y and ${\beta }_3$ is the coefficient of the interaction term, X and W. The third hypothesis is:

Hypothesis 3 (H3).

Financial support packages from fiscus interventions moderate the COVID-19 health regulations andsocioeconomic dimensions (3a to 3e).

3. Methods

The ethical clearance of the study was obtained with reference FCRE2022/FR/04/006-MS(2). An explicit informed consent was obtained from the participants who participated in the survey. The research used cross-sectional quantitative research based on the post-positivist paradigm [94,95]. The population consisted of households in four selected South African Provinces, Eastern Cape, Gauteng, Kwa-Zulu Natal, and Limpopo. These Provinces cover two-thirds of South Africa’s population (66.5%) [96]. The sample size was calculated using Cochran’s [97] sample size estimation formula:

$$n={\displaystyle \frac{Z^{2}P(1-P)}{d^2}}$$

(5)

where n represents sample size, while P = expected proportion, Z statistic, Z = 1.96, and d = precision. The sample size was 384, and the research used a non-probability sampling method employing a convenience sampling technique. A total of 302 responses from this self-administered survey were obtained from different households, equating to 78.6%. This was higher than the average online survey of 44.1% [98]. The respondents comprised 30.43% male and 69.57% female, where 43.5% were between the ages of 36 and 45 years, 30.2% were aged 35 years or younger, and 26.2% were older than 45 years. Of these respondents, 71.2% lived in the suburbs or city center, 21.9% lived in townships, and 6.90% lived in a village or a farm. The size of households: 36.09% of the respondents had a household size of 3 members or less, 41.39% had a household size of 4 to 5 members, and 22.18% had a household size of more than 5 members.

A 30-variable instrument using a 7-point Likert scale from “strongly disagree” to “strongly agree”. was developed from the literature focusing on the constructs related to employment levels, poverty, life quality, health and well-being, family and social support, COVID-19 health regulations, and fiscal support. Appendix A provides the detailed survey questionnaire. The data were analyzed with IBM Statistical Package for Social Science (SPSS) version 28 and SmartPLS 4 [99]. The empirical data were initially screened and cleaned, starting with the extreme outliers, using z-scores with guidelines of ±3.29 [100]. This was followed by missing value analysis to assess the levels of the missing values, and there were no issues with all variables having missing values of less than 10% [101]. The overall instrument internal consistency was acceptable with Cronbach’s Alpha, α = 0.765 for the 30 items [102]. The PLS-SEM measurement model evaluated the quality, and the structural model tested the hypotheses of the study [103].

4. Results

4.1. Measurement Model

The structural equation modeling partial least square (PLS-SEM) using SmartPLS 4, was employed to determine the convergence validity, composite reliability, and discriminant validity (

Table 1. Convergence validity and reliability from the measurement model.

Construct	Variables	M	SD	Λ	α	ρA	ρc	AVE
Poverty level (PVL)	VAR1	5.27	2.075	0.880	0.868	0.870	0.919	0.792
	VAR2	5.36	1.984	0.931
	VAR3	5.41	1.844	0.857
	VAR4	6.55	0.686
Employment level (EMP)	VAR5	4.64	2.253	0.879	0.878	0.897	0.911	0.674
	VAR6	4.94	2.116	0.854
	VAR7	4.39	2.234	0.753
	VAR8	4.16	2.304	0.876
	VAR9	3.68	2.217	0.732
Life quality (LQT)	VAR10	5.95	1.134		0.786	0.882	0.854	0.598
	VAR11	6.25	0.767	0.622
	VAR12	6.37	0.627	0.734
	VAR13	5.58	1.496	0.889
	VAR14	5.19	1.653	0.821
Health and well-being (HWB)	VAR15	5.12	1.774	0.702	0.8	0.842	0.864	0.616
	VAR16	5.09	1.816	0.708
	VAR17	3.5	2.1	0.854
	VAR18	2.84	1.937	0.861
Family and social support (FML)	VAR19	4.79	2.059		0.746	0.825	0.853	0.665
	VAR20	4.16	2.016	0.884
	VAR21	3.7	1.956	0.903
	VAR22	4.98	1.661	0.631
	VAR23	5.66	1.421
Relief fund (RLF)	VAR24	3.2	2.151	1
Support package (SUP)	VAR25	3.44	1.948	1
COVID-19 health regulations (COV)	VAR26	4.97	1.676	0.849	0.884	0.898	0.915	0.685
	VAR27	4.95	1.772	0.87
	VAR28	4.96	1.745	0.896
	VAR29	5.7	1.241	0.727
	VAR30	5.63	1.558	0.785

Empty cell—variable excluded in the final measurement model due to low factor loadings; RMSR = 0.065.

).

The final model comprised of 26 variables, with four excluded (empty cell) due to low factor loading (λ). This is critical for ensuring the validity and reliability of the measurement model. Low factor loadings indicate that an indicator does not adequately represent the underlying construct, potentially leading to erroneous conclusions. The factor loadings in the final model all had factor loadings ≥ 0.70 except VAR11 and VAR22, which were higher than 0.6, with the average loading factor for the items of their respective constructs still higher than 0.7 and were thus retained. The model’s fit was further assessed using the root mean square residual (RMSR) [93]. The SRMR measures how well the PLS-SEM model fits the data, and it fits well in this model with RMSR = 0.065, which is better than a threshold of 0.08 [104]. The convergence validity of the constructs was assessed using the Average Variance Extracted (AVE). The results indicate that all constructs demonstrated convergence validity, as their AVE values were all above 0.5 [93], ranging from the lowest AVE = 0.598 for LQT and the highest AVE = 0.792 for PVL. Composite reliability with ρ_A and ρ_c, and α confirmed the reliability of the constructs with all values ≥ 0.70 [105].

The discriminant validity was assessed with a heterotrait–monotrait ratio (HTMT) matrix [106]. The heterotrait–monotrait ratio (HTMT) quantifies the comparison between the average correlations among different constructs (heterotraits) and the average correlations within the same construct (monotraits).

Table 2. Discriminant validity with heterotrait–monotrait ratio (HTMT)—matrix.

		EMP	FML	HWB	COV	PVL	LQT	RLF	SUP	SUPP × COV
Heterotrait– monotrait ratio (HTMT)— matrix	EMP
	FML	0.117
	HWB	0.713	0.113
	COV	0.061	0.376	0.120
	PVL	0.712	0.103	0.536	0.060
	LQT	0.444	0.392	0.370	0.226	0.422
	RLF	0.358	0.112	0.275	0.133	0.344	0.162
	SUP	0.139	0.208	0.119	0.303	0.067	0.070	0.171
	SUPP × COV	0.052	0.073	0.063	0.280	0.038	0.066	0.041	0.010

indicates that all the values in the matrix were less than 0.85 (HTMT_0.85), confirming discriminant validity [107].

4.2. Structural Model and Hypothesis Testing

The structural model diagnostics assessment was performed to evaluate the presence of multicollinearity using the Variance Inflation Factor (VIF) (

Table 3. Path coefficients from structural models.

Effects	Path	β	t-Statistics	p-Value
Total Effects	COV- > EMP	−0.081	1.239	0.215
	COV- > FML	0.324	5.317	0.000
	COV- > HWB	−0.085	0.997	0.319
	COV- > PVL	−0.047	0.576	0.565
	COV- > LQT	0.215	2.683	0.007
	COV- > RLF	0.126	2.225	0.026
	RLF- > EMP	0.341	6.683	0.000
	RLF- > FML	0.050	0.837	0.403
	RLF- > HWB	0.261	4.405	0.000
	RLF- > PVL	0.328	6.413	0.000
	RLF- > LQT	−0.202	2.619	0.009
	SUP- > EMP	0.110	1.806	0.071
	SUP- > FML	0.100	1.665	0.096
	SUP- > HWB	0.082	1.208	0.227
	SUP- > PVL	0.035	0.513	0.608
	SUP- > LQT	−0.065	0.974	0.330
	SUP × COV- > EMP	−0.009	0.161	0.872
	SUP × COV- > FML	0.135	2.817	0.005
	SUP × COV- > HWB	0.017	0.238	0.812
	SUP × COV- > PVL	−0.002	0.005	0.996
	SUP × COV- > LQT	0.015	0.309	0.757
Direct	COV- > EMP	−0.124	1.989	0.047
	COV- > FML	0.318	5.167	0.000
	COV- > HWB	−0.118	1.413	0.158
	COV- > PVL	−0.088	1.109	0.267
	COV- > LQT	0.241	3.230	0.001
	COV- > RLF	0.126	2.225	0.026
	RLF- > EMP	0.341	6.683	0.000
	RLF- > FML	0.050	0.837	0.403
	RLF- > HWB	0.261	4.405	0.000
	RLF- > PVL	0.328	6.413	0.000
	RLF- > LQT	−0.202	2.619	0.009
	SUP- > EMP	0.110	1.806	0.071
	SUP- > FML	0.100	1.665	0.096
	SUP- > HWB	0.082	1.208	0.227
	SUP- > PVL	0.035	0.513	0.608
	SUP- > LQT	−0.065	0.974	0.330
	SUP × COV- > EMP	−0.009	0.161	0.872
	SUP × COV- > FML	0.135	2.817	0.005
	SUP × COV- > HWB	0.017	0.238	0.812
	SUP × COV- > PVL	−0.002	0.005	0.996
	SUP × COV- > LQT	0.015	0.309	0.757
Effects	Path	β	t-statistics	p-value
Specific indirect effects	COV- > RLF- > PVL	0.041	2.052	0.040
	COV- > RLF- > FML	0.006	0.718	0.473
	COV- > RLF- > LQT	−0.025	1.554	0.120
	COV- > RLF- > HWB	0.033	1.979	0.048
	COV- > RLF- > EMP	0.043	2.092	0.036

Variance Inflation Factor (VIF) = 1.000–3.440.

). The VIF values in the sample were below the recommended threshold of 5, demonstrating the absence of multicollinearity concerns [93].

4.2.1. Direct Relationship

The PLS-SEM structural model was determined using bootstrap t-values (5000 sub-samples). The results show that there is a statistically significant negative relationship between COVID-19 health regulations and employment levels (H1b: β = −0.124, t = 1.989, p < 0.05). The results also show a statistically significant relationship between COV and LQT (H1d: β = 0.241, t = 3.230, p < 0.01) and COV and FML (H1e: β = 0.318, t = 5.167, p < 0.001). There was no statistically significant relationship between COV and HWB and COV and PVL with p-values greater than 5%. As such, hypotheses H1b, H1d, and H1e are supported, while H1a and H1c are not supported.

4.2.2. Mediation Analysis

The mediation analysis was performed to determine the mediating role of relief funds (RLF) in the relationship between COVID-19 health regulations (COV) and socioeconomic dimensions (SOE) (Table 3). The results revealed a statistically significant indirect effect of COV on PVL through RLF (H2a: β = 0.074, t = 2.643, p < 0.05). The total effect of COV on PVL is not statistically significant (β = −0.036, t = 0.540, n.s.), and with the inclusion of the mediator, the direct effect of COV on PVL was statistically significant (β = −0.110, t = 1.685, n.s.). The results indicate that there is full mediation and support H2a. The results also indicate a statistically significant indirect effect of COV on EMP through RLF (H2b: β = 0.043, t = 2.092, p < 0.05), with the total effect of COV on EMP not statistically significant but the direct effect of COV on EMP statistically significant (β = −0.124, t = 1.989, p < 0.05), thus indicating a partial mediation effect and support H2b. RLF also has a partial mediation effect in the relationship between COV and HWB (H2c: β = 0.033, t = 1.979, p < 0.05). This means that the relief fund interventions demonstrated partial mediation, as they supported communities directly by cushioning the effect of COVID-19 health regulations on health and well-being. RLF does not have a mediation effect in COV and LQT, similarly in COV and FML, and as such H2c and 2e are not supported.

4.2.3. Moderation Analysis

The moderation analysis results show that SUP interventions moderate the relationship between COV and FML, SUP × COV- > FML (H3e: β = 0.135, t = 2,817, p < 0.01). The simple slope analysis shows that at higher SUP (+1SD), the COV has a higher impact on FML, while at low SUP (−1SD), increased COV has a low impact on FML (Figure 2). This means that with the increase in COV, the FML increases because the SUP increases (steeper gradient). SUP does not have a moderating effect in other relationships in the model; as such, H3e is supported, while Ha–Hd is not supported.

4.2.4. Multigroup Analysis

Multigroup analysis was employed in the SEM to determine the mediation and moderation effects changed across the different demographic parameters—gender, age, area of stay, and number of members in a household (

Table 4. Multigroup analysis.

	Gender	Age			Area of Stay			Members of the Household
Path	Female– Male	35–45 Years and ≤35 Years	35–45 Years and >45 Years	<35 Years and >45 Years)	Suburb or City Center— Township	Suburb or City Center— Village or Farm	Township— Village or Farm	≤3 Member—4–5 Members	3 Members or Less to >5 Members	4–5 Members—<5 Members
COV - > RLF-> PVL	−0.044	0.046	−0.078	−0.124 *	−0.001	0.031	0.031	0.039	0.052	0.013
COV - > RLF -> FML	−0.006	0.006	0.031	0.025	0.007	0.011	0.004	0.006	−0.026	−0.032
COV - > RLF -> LQT	0.051	−0.035	−0.079	−0.044	0.047	−0.008	−0.055	−0.031	−0.003	0.028
COV - > RLF -> HWB	−0.070	0.028	−0.039	−0.067	0.031	0.028	−0.003	0.045	0.016	−0.029
COV - > RLF -> EMP	−0.068	0.043	−0.078	−0.121 *	−0.004	0.038	0.041	0.038	0.035	−0.003
SUP × COV - > EMP	−0.021	−0.035	0.074	0.108	−0.082	−0.007	0.075	0.047	−0.188	−0.236
SUP × COV - > FML	−0.127	0.250 *	−0.046	−0.296 *	−0.003	0.437	0.440	−0.057	−0.052	0.005
SUP × COV - > HWB	−0.095	−0.201	0.066	0.267	−0.037	−0.092	−0.055	0.119	−0.112	−0.232
SUP × COV - > PVL	0.079	−0.350 *	−0.136	0.213	0.071	−0.096	−0.167	0.315 *	−0.028	−0.343
SUP × COV - > LQT	0.234 *	0.219	0.214	−0.005	0.189	0.355	0.166	−0.024	0.178	0.202

* p < 0.05.

). The results show that there was no statistically significant difference between females and males except for SUP moderation of COV and LQT. These results show that SUP moderated the relationship between COV and LQT more for females than males. We also compared the different age groups, and there were several statistically significant differences between them; RLF mediated the relationship between COV and PVL stronger for age groups older than 45 years than those 35 years and younger. The same pattern was also evident for RLF on COV and EMP.

SUP moderated COV and FML better for those 45 years and older than 35 years and younger, and similarly for 35–45 years than 35 years and younger. SUP moderated the relationship between COV and PVL between 35 years and younger than 35–45-year-olds. We compared the different areas of stay, suburbs or city center, townships, and villages or farms. The results showed no statistically significant difference for the different structural paths. Similarly, there were no statistically significant differences between the different groups of members of the households (3 members or less, 4–5 members, more than 5 members) except one, where SUP moderated better the relationship between COV and PVL for those having a 3-member household or less than those with 4–5 members of the households.

5. Discussion

COVID-19 health regulations comprised of measures that prevented non-essential activities outside the home, set limitations on all public meetings, resulted in the closure of all schools, and implemented a curfew. These regulations also prohibited the sale of tobacco goods and spirits and established stringent internal and international travel controls [108] at Alert Level 5, with the measures relaxed at the changes to lower alert levels. In response to the unprecedented impact of COVID-19, governments around the world intervened with emergency health regulations such as mandatory non-pharmaceutical interventions, social distancing, quarantine, and periodic lockdowns which include restrictions on movement (curfew) [57,109,110]. This was critical as COVID-19 threatened health systems and citizen life in countries.

This paper investigated fiscal support interventions on health regulations and socioeconomic dimensions. Through survey analysis, we found that the relief fund, which entailed the COVID-19 Social Relief of Distress allowance of R350 per month, unemployment insurance fund claim, and food parcel distribution, among others, had a full-mediation effect on the relationship between COVID-19 health regulations and poverty levels. This confirms the effectiveness of government intervention on poverty with fiscal intervention [24] to mitigate the increased poverty that was exacerbated by COVID-19 and its associated health regulations [111]. The results also reveal that relief funding has a competitive partial mediation effect between COVID-19 health regulations and employment levels. Unsurprisingly, relief funds had an intervening role as poverty and employment are generally intertwined [68]. Noticeably, though, it is a competitive mediation that occurs in a situation where both indirect and direct effects are present, but they have opposite directions, also known as inconsistent mediation [112]. This implies that there are other factors that influence employment levels. This is not surprising considering the chronic challenges of high unemployment levels in South Africa. South Africa had regressed over the years to remain at exceptionally high levels of unemployment compared to international standards. Bassier et al. [111] posited that between February and April 2020, there was a significant 40% decrease in the number of people employed. Out of this fall, half of it was due to job terminations rather than temporary furloughs. The official unemployment rate climbed by 1% from quarter 4 in 2019, before the start of the COVID-19 pandemic, and quarter 1 in 2020 to 30.1%, then further continued to deteriorate, peaking at 35.3% in quarter 4 in 2021, then improved post COVID-19 to 31.9% in quarter 3 of 2023 [113]. Despite the improvement, it still has not reached the pre-COVID-19 unemployment level of 29.1% [113].

The results also revealed that relief funds fully mediate the relationship between COVID-19 health regulations and health and well-being. This meant that the relief fund contributed toward health and well-being, which included stress, anxiety, and other related mental illnesses which had increased due to COVID-19 [64]. The crisis theory posits that individuals experiencing a condition of crisis exhibit heightened anxiety and are receptive to assistance. Thus, the rationale behind crisis theory is the conviction that offering assistance and direction to those in crisis will result in long-term mitigation of mental health issues. This aligned the reasoning behind the relief fund mediating the COVID-19 regulations and health and well-being. Relief funds did not have a mediation effect on COVID-19 regulations, life quality, or family and social support. The lack of mediation effect on life quality might be attributed to the comprehensive social service offered by the government, in particular, free water and electricity for certain households [72,114,115] and free health services and access in the event of illness [73] which remained available even during COVID-19. The results also reveal that the R500 billion government support package moderates the relationship between COVID-19 health regulations and family and social support. The COVID-19 pandemic has had enormous impacts on the health system, society, and economy of countries around the world, including South Africa. Invoking emergency regulations and adopting national health regulations such as lockdowns significantly impacted the country’s health and economy. Despite the COVID-19 health regulations helping to protect the lives of the communities, the regulations negatively impacted the economy, which was partially mitigated by the fiscal support interventions. The impact of COVID-19 regulations on socioeconomic dimensions was fairly similar irrespective of gender, number of members in a household, or areas of stay, notwithstanding that one situation each. However, there were some patterns with age, where the impact was less on the 35-year-olds and younger for poverty and unemployment. This is not surprising, considering that almost 60% of the youth in South Africa are unemployed [116]. Despite this, governmental interventions in a society can range from policy and programs [51]. This highlights the alignment to the theory of intervention, especially the mechanism of how to intervene, and when and where a concrete social intervention should occur [49,50]. The results though show that it was important for the government to consider the extent and scope of interventions based on the specific nature of the crisis [53]. The COVID-19 pandemic revealed the deep intersection and complementarity between crisis and intervention theories, where understanding COVID-19 regulations (crisis theory) enabled more effective and targeted relief funds (intervention theory) that addressed both immediate and structural socioeconomic challenges.

6. Conclusion, Implications, and Suggested Future Directions

The COVID-19 pandemic outbreak brought the crisis central with the COVID-19 health regulations and fiscal support interventions in the attempts by the governments to minimize the impact on society. We performed a post-COVID-19 analysis of fiscal support interventions on health regulations and socioeconomic dimensions. We concluded that COVID-19 regulations impacted socioeconomic dimensions, and the relief fund was a critical intervention to mediate poverty and employment. We can also conclude that other government support packages, which encompass loan guarantees, job support, tax and payment deferrals, social grants, wage guarantees, health interventions, and municipality support, helped mitigate the impact of COVID-19 health regulations. These results confirm the effectiveness of the government’s budgetary support intervention in reducing the impact of its emergency response to COVID-19 health standards. This supports the notion of intervention and emphasizes its intricacy, underscoring the significance of comprehensive intervention for future readiness and improving the perspective on crisis intervention.

6.1. Policy Implications

The multi-nature and complexity of the COVID-19 pandemic require understanding to develop holistic interventions that will balance health and economic disruptions in society. The analysis in this study is imperative for the government to respond to shocks that result in chaos that continue to occur at heightened levels of pandemic or other major events that disrupt the societies to build knowledge as there are insufficient tools to identify emerging threats and opportunities in a complex environment. Based on the empirical findings of this study, policymakers should focus on the determinants of economic stimulus of COVID-19 and design more economic stimulus packages in such a way that they would be able to mitigate the negative effects of the COVID-19 pandemic. There is also a need for an improved social relief policy. These can be achieved through developing sociodemographic specifics like age-specific initiatives and socioeconomic-specific interventions guided by ‘triple-challenges’ of poverty, employment, and inequality for developing countries such as South Africa. There is also a need to strengthen the systems and processes to ensure the policy interventions succeed. This can be achieved by also strengthening the legal framework and anti-corruption policy. This is necessary considering that despite the positive impact of the fiscal interventions, there were issues such as corruption [117,118,119] which was even evident in food parcels that were part of the relief fund [120] and delays [24] in execution that comprised its full effectiveness.

6.2. Theoretical Implications

The theory of intervention is essential to ensure that interventions are not merely a collection of random activities but are carefully planned and executed with a thorough comprehension of the anticipated mechanisms of change. It increases the probability of success and offers a structure for assessment and continuous improvement. Having an intimate understanding of the crisis helps to plan intervention better, minimizing the negative consequence of the intervention. Furthermore, it highlights the importance of inter- and intra-crisis learning. Quaglia and Verdun [65] explained that the process of policy learning gained from its experience with previous crises (inter-crisis learning) and valuable lessons (intra-crisis learning) are central to the management of the crisis as this helps to enhance the importance of timing (quick action). Within the context of the study, there is a deep intersection and complementarity between crisis and intervention theories, thus elevating a need to advance a crisis–intervention perspective.

6.3. Limitations and Suggestions for Future Research

The research was not without limitations, noticeably the convenience sampling and inadequate balance in respondents, for example, with gender, where females dominated compared to males. Additionally, not all South African provinces were covered in this study. This means that generalization of the study must be treated with caution [121,122]. Despite this, the study provides critical insights necessary for policy improvement and theoretical contributions. For future research, we recommend expanding the study to include all the provinces and possibly conducting a comparative analysis between developed and developing countries. This will validate our findings and provide lessons that can help optimize the interventions. Furthermore, a study should be conducted on all the different elements of fiscal support to understand the ones with a higher impact than the others. The necessity of this analytical route was evident in this study, where the relief fund’s importance was advanced as part of the bigger fiscal support package.