Savings and family functioning since the 2008 recession: An exploratory study in Singapore

Abstract

Using a survey of 200 low- to moderate-income households in Singapore, this study analyzes how changes in economic resources (in particular assets) are associated with family strains and family functioning. Key findings of this study are as follows: 1) a majority of the sample experienced reduced economic resources; 2) a reduction of savings increased family strain; 3) households experiencing decreases in savings have lower levels of family functioning measured by family social support and family cohesiveness. This article concludes with several implications for policy and practice.

Keywords

economic recession family cohesiveness family strain family support savings

Introduction

Stemming from the 2008 global economic downturn, a large portion of families have experienced unemployment and a plummeting decrease in their disposable incomes. Economic recessions have been shown to have a profound impact on working parents, children, and families (Solantaus et al., 2004). Although the recession’s effects are rippling across all income levels, low-income households are particularly vulnerable to increased stress as they are likely to have insufficient economic resources to buffer the negative impacts brought about by the economic upheaval. Regardless of their practice choice, social workers across the globe are no doubt confronting this heightened family stress.

A number of factors explain why some families respond better than others to economic disruption. Studies have shown that resilient families have the capacity to overcome adversity and stressors by adapting to the changed environments in order to maintain a level of functioning (Lazarus, 2004; McCubbin, 2001). Social support and cohesiveness are important dimensions of family functioning that are associated with a family’s ability to be resilient (Lazarus, 2004). In addition to social support and cohesiveness, economic assets or savings may be one of many protective factors that can help families overcome economic hardships. Because social workers work directly with families experiencing these stresses, it is important for social workers to better understand the key factors that explain the economic stress process.

People save money to lessen the impacts of financial hardships as well as promote individual and family development through future investment (Sherraden, 1991). Additionally, assets are resources that provide families with the capacity for taking risks. Generally, assets can be defined as ‘rights or claims related to property, both tangible and intangible’ (McKernan and Sherraden, 2008: 1). In certain contexts, assets can also include intangible networks such as social capital. However, in this article assets refer more narrowly to both financial (savings in checking and savings accounts, stocks, and bonds) and non-financial (housing, land, and business) assets. Although positive functions of asset ownership are enormous, the relationships between asset ownership and family functions have only recently been examined in the family literature (Dew, 2007; Rothwell and Han, 2010).

The social work literature is scant on theories about how families experience and respond to stress. This study aims to advance the social work understanding of how lower-income families adjust to the economic realities brought about by the dramatic 2008 recession. Specifically, the study contributes to the social work knowledge base in three ways. First, the study explores the extent to which the family economic stress model can be applied to the Singapore context. Second, informed by the asset-based theory of social wellbeing (Sherraden, 1991), we examine whether changes in savings since the recession relate to family-level functioning. Relatively few studies have examined the ways in which asset ownership influences family-level outcomes. Lastly, we narrow the focus to low- to middle-income Singaporean households who are more likely to experience heightened strains of the recession and explore the extent to which the family stress model and the asset theory explain family functioning.

2008 economic recession in Singapore

Singapore is a city-state located at the end of the Malaysian Peninsula in Southeast Asia. It is small in terms of size at 712.4 square kilometers and with a population of 5.08 million (a resident population of 3.77 million) in 2010. Singapore is a multi-ethnic society where people of Chinese ethnicity are the majority (74.2%), followed by Malays (13.4%), Indians (9.2%), and others (3.2%). As a country, Singapore has achieved miraculous economic growth since achieving independence from Britain in 1963 and separating from Malaysia in 1965. In 2008, Singapore’s per capita Gross National Income was US$37,419, compared to US$44,187 in the UK (Singapore Department of Statistics, 2011).

The 2008 global financial crisis can be understood as a series of linked crises which has caused an economic slowdown and a number of social challenges in many countries worldwide (Farnsworth and Irving, 2011). Singapore is not an exception. The country is particularly vulnerable because the small open economy is heavily dependent on exports and foreign trade and can be seriously influenced by the global economic turbulence (Austin, 2009). The Singapore unemployment rate of residents increased from 3.1 percent in June 2007 to 4.6 percent in June 2009 (Ministry of Manpower, 2010). Real economic growth was negative (-2%) in 2009. Because economic fluctuations have been frequent (for example, negative real economic growth of -1.4% and -2.4% in 1998 and 2001 respectively [Singapore Department of Statistics, 2010]) and are expected to continue, it is important for the professional social work community to understand how such economic conditions affect family functioning.

Knowledge about how Singaporean families have responded to the latest recession is rather limited. One survey of 817 Singaporeans by the Institute of Policy Studies (IPS) reported several findings. First, significant percentages of families (p < .05) reported decreases of savings (36%) and income (29%), and experienced a reduction in employment (23%). Second, low-income households (defined by an income level of S$2500 or less) compared to the overall sample reported more severe decreases in savings (49%) and income (46%), and reduced employment (33%). Respondents cited savings as the most important coping strategy to counter economic hardship brought about by the recession. Other strategies included ‘Getting any job regardless of pay’ and reliance on family and friends. For the sub-sample of low-income households, savings was the first coping strategy, but was followed by family and friends and ‘Getting any job regardless of pay’ (IPS, 2009).

Scholars have raised concerns that the most recent economic recession may have harsher impacts on vulnerable groups in Singapore (Ng and Rothwell, 2009). In particular, they suggest that low-income households and single-parent families could be more vulnerable to the economic downturn.

Assets and family functioning: Theory and evidence

The Family Economic Stress Model

The Family Economic Stress Model (FESM) delineates how economic stress leads to negative family functioning and poor child development outcomes such as anxiety, depression, and health problems (Conger and Elder, 1994; McLoyd, 1990). The model suggests an indirect influence of economic strain on child well-being. Accordingly, economic pressures increase parents’ psychological stress, and this strain may compromise the marital relationship and parenting practices and therefore negatively affect children. As a result, children in households experiencing economic stress are at a higher risk for poor developmental outcomes including academic participation and achievement, social development, and health outcomes.

Extending the FESM, Solantaus and colleagues (2004) examined how a nationwide economic recession in Finland influenced family process and children’s mental health. They found that income reduction during the 1990 to 1994 economic recession indirectly deteriorated children’s mental health through financial pressure, negative marital interaction, and parenting quality. Outside of the US and Europe, there have been no known studies of the FESM.

While the FESM is helpful in understanding family stress, poverty and financial strain are not static conditions. Rank and Hirschl (2001) documented the high likelihood of falling in and out of poverty across the life course. Further, at least one longitudinal study showed that financial strain fluctuates and actually declined over time in the UK (Tomlinson and Walker, 2010).

Asset ownership and family functioning

Asset ownership is proposed to positively influence the social welfare of individuals and families (Scanlon and Page-Adams, 2001; Shapiro, 2001; Sherraden, 1991). Most of the existing research on the influence of assets has been at the individual level (see Lerman and McKernan, 2008). Further, asset ownership has been shown to influence earnings mobility (Morillas, 2007) and improve educational outcomes for children (Zhan and Sherraden, 2003). A small but growing evidence base is refining the understanding of how asset ownership matters at the family level (Rothwell and Han, 2010). Access to financial assets was shown to mitigate economic hardships in a random sample of the White middle- and working-class in the US states of New Jersey, Ohio, and Tennessee (Parks-Yancy et al., 2007). Furthermore, studies showed that assets affected marital relations as married couples owning assets were more likely than couples without assets to report marital satisfaction (Dew, 2007). Similarly, Gudmunson and colleagues (2007) found that financial resources were positively related to marital stability partially via reduced financial strain. Economic mobility is one possible mechanism that relates asset ownership to reduced family strain (Morillas, 2007; Ng and Rothwell, 2009).

Linking assets to family functioning

Low-income households are likely to have fewer possessions and savings, and thus are more vulnerable than middle- and upper-income households to the financial hardships of unemployment (Han, 2009). Furthermore, low-income families compared to middle- and upper-income households have less access to financial and other banking services that protect and grow wealth (Barr and Blank, 2009).

When low-income households experience an economic recession they are likely to encounter financial strain (Han, 2009). In this situation, low-income households are likely to struggle to make ends meet and pay for basic needs including food, medical care, and housing. The Family Adjustment and Adaptation Response (FAAR) model is one framework that interprets how the family stress and strains influence family processes and ultimately how families adapt and adjust to the strains (McCubbin and Patterson, 1983). Changes in not only income but also assets may be considered stressors which increase ‘demand placed on the family that produces, or has the potential to produce, changes in the family system’ (McCubbin et al., 2001: 17). Depending on the ratio of demands to resources and the family patterns of functioning, families are likely to have differing levels of adaptation to financial strains. The family adaptation process produces new patterns of family dynamics in terms of rules, relationships, support, communications, and interaction with the community (McCubbin et al., 2001). When families have sufficient resources to endure the strain, they are considered resilient. Recently, Rothwell and Han (2010) found that assets were positively associated with perceived economic strains and that association is partially mediated by negative financial events. Based on this rationale, assets and savings may be particularly influential factors in promoting family resiliency.

Building upon this research, the current study examined the family functioning processes following the 2008 recession. Specifically, we analyzed how changes in economic resources (in particular assets) were associated with family strains and family functioning. This study addressed three research questions using data gathered in a survey of low- to moderate-income households in Singapore: 1) How have low- to moderate-income households experienced the 2008 economic recession?; 2) How are economic resources, in particular, a reduction in savings, related to family strain?; 3) To what extent are assets associated with family functioning measured by family support and family cohesiveness?

Methods

Data and sample

In 2009, a market research company was commissioned to survey 200 households whose incomes were below the Singapore median level (S$5000). The purposive sampling strategy targeted income-eligible Singapore residents in and around their housing blocks and commonly frequented shopping malls. The study was approved by the Institutional Review Board at the National University of Singapore (NUS IRB No. 09-219).

Measures

The survey questionnaire asked a number of questions about how families experienced the recession and how their family had responded. For example, one question asked, ‘Since the current recession began in 2008, would you say your economic status is better, worse or no different?’. Participants were asked to respond with one of three responses (‘worse off’, ‘no difference’, or ‘better off’) in terms of employment, income, and savings.

This study also explored how asset poverty related to key dimensions of family functioning. Asset poverty has been defined as, assuming no income or earnings, insufficient savings and financial assets to provide for basic needs for three months (Haveman and Wolff, 2005). Based on this definition of asset poverty, we asked respondents subjectively how long they could rely on savings in case of income loss. Therefore, households in this study who responded that they could not provide for basic needs for three months were coded as 1.

Family functioning was operationalized with two measures of family cohesiveness and family support which were derived from the Family Hardiness Index (FHI) and the Social Support Index (SSI). These two measures were based on the work of McCubbin and Patterson (1983) and McCubbin et al. (2001). Family cohesiveness was one of the dependent variables in the study. We used seven items measuring family cohesiveness from the FHI. Examples of items included: ‘We have a sense of being strong even when we face big problems’, ‘Many times I feel I can trust that even in difficult times things will work out’, and ‘While we don’t always agree, we can count on each other in times of need.’ Participants responded to these questions on a four-point scale ranging from ‘Mostly false’ (1) to ‘Mostly true’ (4). An exploratory factor analysis found that a one-factor model of the seven items was acceptable and explained about 50 percent of the variance. Factor loadings ranged from .34 (‘We have a sense of being strong even when we face big problems’) to .85 (‘We work together to solve problems’). A reliability test of Cronbach’s α (a higher value indicates higher internal consistency among variables) found that the seven items had an acceptable level of internal consistency (Cronbach’s α = .82).

A total of six items from the SSI, a Likert-style measure with five points ranging from ‘Strongly disagree’ (1) to ‘Strongly agree’ (5), were used to measure family support. The scale intends to measure levels of social support among family members. Examples of items included: ‘I feel good about myself when I sacrifice and give time and energy to members of my family’, ‘The things I do for members of my family and they do for me make me feel part of this very important group’, and ‘I have friends who let me know they value who I am and what I can do.’ A one-factor model was supported by an exploratory factor analysis with about 49 percent of the variance explained by the six items. Factor loadings ranged from .30 to .94. A reliability test found that the SSI had a moderate level of reliability with Cronbach’s α of .76.

Questions about family strains asked if they had experienced in the past year, ‘Problems/issues that don’t get resolved’, ‘Tasks/chores which don’t get done’, and ‘Strain on family money for medical expenses, clothes, food, education, home care, etc.’ They were measured by a Likert-type scale ranging from ‘Never’ (1) to ‘Almost always’ (5). Exploratory factor analysis found that a one-factor model explains about 67 percent of the variance. Factor loadings range from .49 to .89 and a reliability test found a moderate level of reliability (Cronbach’s α = .76).

Control variables included age, gender, race (Chinese = 1; Others = 0), marital status (married = 1; others = 0), household size (sum of numbers of children and adults in household), education (primary school or less = 1; secondary school = 2; secondary school graduation = 3; polytechnic or Institute of Technical Education = 4; four-year university = 5; and graduate level or higher = 6), homeownership, and total debts. The total debts had seven ordinal categories (less than S$1000: 1; S$1000–S$5000: 2; S$5001–S$10,000: 3; S$10,001–S$30,000: 4; S$30,001–S$50,000: 5; S$50,001–S$100,000: 6; and S$100,001 or more: 7). In terms of income status, households with income up to S$2000 were considered low income and were coded as 1, middle-income households were those with incomes greater than S$2000 and coded with 0. The IPS study used S$2500 or 50 percent of the median income as a threshold to divide low- and middle-income households. This study could not use S$2500 at the point because income was measured at S$1000 intervals. As a result we used a more conservative S$2000 or 40 percent of the median income as a low-income threshold.

Data analysis

Descriptive statistics and bivariate analyses were employed to preliminarily investigate how the recession influenced participants. Multivariate regression models using Ordinary Least Square (OLS) were then used to test whether decrease in savings related to family functioning. As the regression models used many economic measures, we examined multicollinearity test using correlation and Variance Inflation Factor. We found that there are no significant violations of multicollinearity problems in the regression models. As the research questions of this study focused on family functioning, the unit of analysis was the family (not the individual). Accordingly, family-level variables included household income, home ownership, family support, and family cohesiveness.

Results

Descriptive statistics

Table 1 presents the descriptive statistics of the sample. The average age was about 39 years. Approximately half of the respondents were female and about 64 percent of them were married. Chinese were the dominant ethnicity in this study (about 57% of the sample), but much lower than the national composition of 75 percent in Singapore (Tambyah et al., 2009). Although the income status of the sample was low- to middle-income households, about 86 percent owned their home.

Table 1.

Descriptive and bivariate statistics of the sample.

	Total sample (N = 200)	Low-income households (n = 54)	Middle-income households (n = 146)	t-statistics or χ²
	Mean (SD) or %	Mean (SD) or %	Mean (SD) or %
Age	39.35 (11.0)	40.48 (11.18)	39.27 (10.78)	.70
Gender: Female (%)	50.26	57.41	48.63	1.21
Race: Chinese (%)	56.61	50.00	60.27	1.70
Marital status: Married (%)	64.55	55.55	64.38	1.30
Household size	4.08 (1.68)	3.66 (1.76)	4.23 (1.63)	2.13*
Home ownership (%)	86.50	74.07	91.09	9.78**
Employment worse- off (%)	44.97	57.41	40.41	4.60*
Income worse-off (%)	51.85	59.26	49.31	1.56
Savings worse-off (%)	62.43	74.07	58.22	4.23*
Expenditure worse off (%)	61.00	74.07	56.16	5.32*
Asset poverty	39.00	59.30	31.50	12.76***
Debts	3.70 (2.34)	3.20 (2.32)	3.88 (2.33)	1.84†
Family social support	24.31 (3.50)	23.39 (4.93)	25.04 (3.69)	2.26*
Family cohesiveness	24.79 (3.04)	24.07 (3.99)	24.99 (2.85)	1.55

Note: For bivariate statistics, t-tests were used for continuous variables and chi-square tests were used for categorical variables.

†

<.10; *<.05; **<.01; ***<.001.

We found that 54 households out of 200 were low-income households. There were no significant differences between low-income households and moderate-income households in terms of age, gender, race, marital status, and number of children in household. However, similar to previous studies (Han, 2009; Institute of Policy Studies, 2009), low-income households compared to the remaining sample reported lower education status, a smaller household size, and a lower home ownership rate. In addition, this study found that a high percentage (39%) of the sample self-reported asset poverty defined by Haveman and Wolff (2005). As expected, low-income households (59%) were more likely than their higher earning counterparts (32%) to report subjective asset poverty (see Table 1).

Research question 1: Impacts of economic recession

Univariate analyses found that a high portion of the sample experienced negative changes in their economic status (see Table 1). For example, since the economic downturn, 45 percent of the sample indicated that they were worse-off in terms of employment and 52 percent experienced income decrease. Savings was most negatively impacted by the recession: about 62 percent of the sample responded to ‘worse-off’, while 34 percent said ‘no difference’.

Further bivariate analyses revealed that low-income households were more severely impacted by the recession as measured by employment, savings, and income. First, low-income households experienced more negative employment than their middle-income counterparts (t = 4.60, p < .05). Second, savings of low-income households were reduced more than their counterparts (t = 4.23, p < .05). While more low-income households experienced decreases in their income when compared to their higher- earning counterparts (59.26% vs 49.31%), the difference was not statistically significant. This finding suggests that the recession has had negative income impacts across the income distribution (see Table 1).

Household expenditures

The evidence showed that household expenditure was negatively influenced by the economic downturn. A majority (61%) of the sample reduced their household expenditure (see Table 2). In addition, two stark difference emerged. First, a majority of the study sample decreased expenditure on buying clothes (73%), leisure (66%), and eating out (62%). Second, a vast majority showed no differences in their expenditure on human capital such as personal education (80%) and children’s education (71%). In fact, a ‘meaningful’ percentage (21%) of the sample increased their investment in children’s education. It is meaningful because this finding suggests that the relatively low-income Singaporeans consider child education to be very important and they have invested more in their children’s education despite the 2008 recession.

Table 2.

Changes in household expenditure.

Expenditure item	Increased expenditure (%)	Decreased expenditure (%)	No difference (%)	Total (%)
Leisure (picnics, outings, movies, sports, travel, etc.)	8.0	65.5	26.5	100.0
Eating out	8.5	62.0	29.5	100.0
Buying clothes	1.0	73.0	26.0	100.0
Spending on alcohol and cigarettes	2.0	19.5	78.5	100.0
Personal education or training	6.0	14.5	79.5	100.0
Children’s education	21.0	8.0	71.0	100.0
Medical treatment	16.5	11.0	72.5	100.0
Housing arrangement	5.5	13.0	81.5	100.0

Research question 2: Family strain

The sample size was reduced to 189 in the regression models due to item-level missing values. We found that a reduction in savings as a result of the recession was positively and significantly associated with family strain. Three other variables were significant predictors in the model, holding other variables constant. First, females were more likely to experience family strains. Second, household size was positively associated with family strain. Finally, lower-income households were likely to have higher family strains (see Table 3).

Table 3.

Regression analysis on family strain.

Variables	b	SE
Constant	4.61***	1.11
Age	.01	.02
Gender
Male (reference)
Female	.91*	.38
Race
Others (reference)
Chinese	−.33	.39
Marital status
Non-married (reference)
Married	.68	.43
Household size	.23†	.12
Home ownership	−.83	.58
Employment worse-off	.03	.51
Income worse-off	.01	.53
Savings worse-off	.87†	.47
Income up to $2,000	1.17**	.44
Asset poverty	−.50	.40
Debts	.19	.24
Adjusted R²	.10
F value (d.f.)	2.69** (12)
N	189

†

<.10; *p < .05; **p < .01; ***p < .001.

Research question 3: Assets and family functioning

Family support

The first family functioning model examined family support (see Table 4). Specifically, the model tested whether decreases in savings were associated with family support. We found that decreases in savings were significantly and negatively associated with family support. In other words, households with decreases in savings were more likely to have lower family support, after controlling for a number of variables. Other measures of economic resources had significant relationships with family support. First, lower-income status and households with asset poverty were likely to have lower family support. Second, households with more debts experienced higher family support. Other socio-demographic measures had no significant associations with family support.

Table 4.

Regression on family support and family cohesiveness.

	Model on family support		Model on family cohesiveness
	b	SE	b	SE
Constant	27.19***	1.51	25.33***	1.40
Age	−.02	.02	.05*	.02
Gender
Male (reference)
Female	.44	.50	−.62	.47
Race
Others (reference)
Chinese	−.62	.50	−.61	.47
Marital status
Non-married (reference)
Married	.47	.56	−.36	.52
Household size	−.03	.16	−.19	.15
Home ownership	−.90	.75	.15	.69
Employment worse-off	−.83	.66	−.16	.61
Income worse-off	1.08	.68	.57	.63
Savings worse-off	−1.32*	.61	−1.22*	.57
Income up to $2000	−1.61**	.58	−.24	.54
Asset poverty	−1.50**	.52	−.18	.48
Debts	.70***	.21	.36†	.20
Family strain	−.23*	.10	−.19*	.09
Adjusted R²	201		.077
F value (d.f.)	4.65*** (13)		2.20** (13)
N	189		189

p < .05; **p < .01; ***p < .001.

Family cohesiveness

The second model examining family functioning appears in Table 4 and presents results of the OLS regression models of the relationships between savings and family cohesiveness. The model showed a direct relationship between a change in savings and family cohesiveness. Households with decreased savings were more likely than the reference group (those who had no change in savings or increases in savings) to have lower family cohesiveness. Two control variables were significantly associated with family cohesiveness: a) older households were likely to express higher family cohesiveness, and b) family strains were negatively associated with family cohesiveness.

Discussion

The recession in Singapore appears to be waning. The last quarter in 2009 showed 3.6 percent growth of Gross Domestic Product. The total exports at current prices at the first quarter in 2010 have also soared by an increase of 29.8 percent. Accordingly, the unemployment rate has been stable at 2.2 percent during the last six months (Singapore Department of Statistics, 2010). Nevertheless, many consider the global economic situation to be tenuous because of instability in the European Union and a slow US recovery.

We found in this study that families experienced negative outcomes related to the recent economic recession. Over half the sample reported worsened economic status in terms of income and savings. In addition, 61 percent of the sample had reduced their living expenditures since the recession. These impacts of the recession were more pronounced among the lower-income group compared to the moderate-income group, as they experienced more reductions in employment status, savings, and expenditures.

The primary finding of this study is that assets in the form of savings matter for family functioning because changes in savings appear to influence family social support and family cohesiveness. We also found that a reduction of savings was positively related to increased family strain, controlling for other variables in the model (the significance level was marginal at the p-value of .10). Additionally, households with lower income are likely to experience higher family strain. These findings suggest that both income and assets may influence how families experience and respond to external economic hardship.

The OLS regression models also show strong relationships between economic resources and family characteristics. First, measures of economic resources (income, asset poverty, debts, and reduction in savings) are significantly associated with family support in the expected direction of the theoretical model. Second, in the model on family cohesiveness, debts and reduction in savings are two measures of economic resources that are negatively related with cohesiveness. Third, we found that the significant associations between assets, family strain, and family functioning support the theory linking economic resources and family relations (McCubbin et al., 2001). In addition, the conclusions are consistent with the findings of Rothwell and Han (2010), where assets may be an influential factor in the family adaptation process. These findings suggest that more research of the possible link between assets and family functioning should be developed and examined.

The positive association between debts and family relations is contrary to findings of previous studies which suggest that high debts may relate to low marital quality (Dew, 2007, 2008). In particular, Dew (2008) reported that consumer debts of recently married couples strongly predicted marital dissatisfaction. Debts in the current study consisted of all types of debts including housing loan and mortgage, car debt, business debt, credit card debt, and debts to relatives and friends. Debts, therefore, do not affect families similarly. Different debts have different impacts on family functioning depending on the types of debts and how the debts are used by families. A home mortgage is quite different than arrears from a credit card in terms of its family strain. The unique Singapore context provides some insight into this research finding. Most importantly, Singapore has a unique housing policy that promotes high homeownership. About 90 percent of households are home owners because of public housing policy by the Housing and Development Board which started in 1960 (Vasoo and Lee, 2006). Singaporeans can use their mandatory retirement savings, called the Central Provident Fund (CPF), to pay for both the down-payment and monthly installments of housing. It may explain how Singaporeans are institutionalized to pay back housing loans and debts without sacrificing family support and cohesiveness. High levels of debts may indicate that they just bought a house. In Singapore, family members may support each other to pay housing debts which may not vitiate family cohesion. On the contrary, housing debts may represent access to home ownership and encourage support and cohesiveness among family members to pay the debts off together. Therefore, housing debts in Singapore may indicate a way to asset building and they may show non-negative impacts on family functioning. Future studies are necessary to identify the links between different types of debts and family resilience in the Singapore context. This conjecture needs to be supported with a representative sample and sophisticated measures.

This study found that females have higher family strain than males. The gender difference in family strain may be ascribed to different strain-inducing experiences where females are likely to spend more time with children and take care of other family members and other household chores (Scott and Alwin, 1989). The different role demands and responsibility to females may explain why females report significantly higher strain than males (Jang, 2007). The indicators of family strain in this study include chores and family money strain for homecare items which are related to high demands of females’ responsibility. Further studies are needed to debunk the gender differences in causes and outcomes of different types of family strain in Singapore.

The findings on the increased investment in children’s education are noteworthy. As Singapore is a small country where people are its only resource, children are regarded as the country’s greatest assets and future (Lee et al., 2008). As a result, social policy in Singapore is oriented toward human capital development. The Singapore government has initiated inclusive education policy through providing financial assistance, developing special schools for disabled children, and implementing measures to prevent at-risk students from dropping out of schools (Lee et al., 2008). These circumstances, despite the economic recession, may lead parents to more investment in children’s education as a path to move upward. Future research would benefit from investigating the impact of these policy mechanisms on human capital investment. This topic would be enhanced greatly with comparative studies of other policy contexts.

Some findings without significant p-values are noteworthy. In the sample, for low-income households and middle-income households there were no differences in age, gender, race, marital status, and number of children in a household. This non-difference between groups can best be explained by the non-representative nature of the sample. We would expect to see larger differences if the sample were nationally representative (e.g. more single parents and higher numbers of children in the low income households). In addition, several socio-demographic measures such as age, gender, race, marital status, household size, and homeownership were found to have no significant associations with the dependent variables (family strain, family support, and family cohesiveness). The insignificant role of marriage was the most surprising finding. Marital status was not significantly related to any of the three outcome variables. From this evidence alone it would be wrong to suggest that marital status has no influence on family functioning. Rather, this finding indicates that the marriage type, duration, and quality need to be more carefully measured in future studies.

Limitations

Several limitations of this study are noteworthy. First, this study uses a convenience sample. Since the sample is not representative, we need to be very careful about generalizing the findings of this study to all of Singapore. Second, the measurement models of this study are exploratory. More sophisticated measurement models of the key factors such as family strains and resilience (Chang and Lim, 2007) should be developed and tested. In addition, the measures of economic resources are based on subjective perception rather than objective information. Finally, this study is based on a cross-sectional survey and the findings do not represent causality. Rather, it suffices to say that we found possible associations between variables.

Implications

The findings of this study support theory and evidence from social work that asset ownership promotes family resilience and development (Scanlon and Page-Adams, 2001; Sherraden, 1991).The findings are unique as they represent the first known study of the FESM outside of the US and Europe. However, more studies are necessary to specify how asset ownership relates to family dynamics in international contexts. In particular, future studies need to develop measurement theory of assets (and debts), family strain, and family resilience among low- to middle-income households. When we use standardized scales of key concepts, we can also develop comparative studies of how the economic downturn has influenced different populations in different countries. It would be important for the field to understand how social workers interpret the factors that influence family stress and the extent to which these perceptions align with models such as the FESM.

Singaporeans may experience different expressions of family strain compared to others in the Southeast Asia region. Singaporeans contribute 20 percent of their income to CPF (McCarthy et al., 2002). In addition, savings in CPF are used to pay debts of public or private housing. Because of these institutional features, lower income Singapore households may have insufficient savings to grapple with financial strains caused by economic downturns. Social policies are needed to encourage low-income Singaporeans to save for housing and retirement, but to save and develop discretionary emergency funds that can be used during economic downturns.

This article provides several implications for social work policy and practice in a context of widespread economic hardship. Some have argued that social policy and programs should be more tailored to suit the unique needs and situations of families (Ng and Rothwell, 2009). One way to achieve responsive policy would be to empower front-line workers with more discretion in determining social assistance eligibility. In this case, social workers could assess the complex realities confronting families on a case-by-case basis rather than solely depending on standardized criteria that may or may not be well suited to the current economic conditions. This discretion would allow social workers to consider recession-related issues such as reduced work hours, increased work responsibility without pay, total assets and debts, and prospects for returning to full-time/fully compensated work. While increased flexibility could make social assistance more responsive to the current situations of families, implementing such a flexible policy presents great challenges for social work. Dean (2006) accurately captured this tension between entitlement forms of distribution and discretionary forms of distribution. According to Dean (2006), flexible policies based on discretion are subject to corruption and subjective prejudices, an argument also posited by Lipsky (1980). In theory, flexible policies may be more suited to the needs of families, but in practice, flexible policies lack institutionalized equity and may lead to unintended consequences.

Despite this tension between entitlement and discretion, this study provides some policy recommendations for consideration when families are struggling with economic downturns. First, financial planning programs can increase awareness about the necessity of saving for rainy days. As low-income households are at higher risk in economic recession, financial planning will be much more important to their family resilience. The Singapore government has been keen to help Singaporeans to be financially literate by initiating an educational program called MoneySENSE (http://www.moneysense.gov.sg). While this program aims to empower families with knowledge and skills about financial decisions and preparedness, there are no known evaluations of its effectiveness. Furthermore, we question the extent to which this program is reaching its intended population. The financial education of social workers is also a growing field. One study showed that a financial literacy curriculum increased knowledge of financial mechanisms (Sherraden et al., 2007). In the US, a Center for Financial Social Work has been established by social workers who wish to build the financial knowledge capacity of social workers, and has developed a financial social work certification (see: http://www.financialsocialwork.com/).

Second, policy-makers can think of more progressive interventions such as asset-building policies which provide institutional opportunities for saving among low-income households. Social workers can play a key role in advocating for these changes. For example, Singapore may introduce Individual Development Accounts (IDAs), which first started in the US but expanded into many countries (e.g. Canada, UK, Korea, and Taiwan). Through a matching mechanism, IDAs encourage low-income households to save regularly to acquire transformative assets such as homes, postsecondary education, and businesses. Matched savings accounts may also encourage savings behavior that could be used to buffer economic hardships and furthermore support family resilience.

To date, the Singapore government responses to the recession have been job- and income-based. Examples of these approaches include the Jobs Credit Scheme, the Skills Programme for Upgrading and Resilience (SPUR), and the Workfare Income Supplement (WIS). The first two schemes intervene at the employer level by helping companies retain employees and sending employees for training; the WIS targets workers directly with one-time cash supplements (Choo, 2010). Based on the findings in this article showing positive relationships between savings and family functioning during a recession, governments might consider empirically tested interventions that promote savings among those with low income. These asset-building interventions for low-income households would complement the income-based approaches that have been implemented by the Singapore government.

Conclusion

This study examined how assets are related to family functioning of Singaporeans since the recent 2008 recession in 2008. The study has shown that not only income but also assets influence family dynamics measured by family support and cohesiveness. Researchers need to examine the dynamic process in a more sophisticated way and the findings may provide strong evidence for asset-building strategies for marginalized populations. This study provides implications for the social work profession in that asset-building strategies can help families overcome negative economic shocks and maintain family functioning. By its nature, social work is a form of asset building (Gregory and Drakeford, 2006). Social workers act as catalysts allowing clients to retake control of their lives and to avoid future problems. Social workers can better understand the stress processes if they consider the role of asset ownership. Asset-building policy and social work possess similar values in helping low-income households avoid potential troubles by developing capabilities and escaping the cycle of poverty. It suggests that social workers should learn about asset-building strategies and consider applying asset-building interventions.

Footnotes

Funding

This research received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors.

Author biographies

Chang-Keun Han is Assistant Professor in the Department of Social Welfare, Sungkyunkwan University.

David W Rothwell is Assistant Professor at McGill University School of Social Work, Canada.

References

Austin

I.P.

(2009) ‘Singapore in Transition: Economic Change and Political Consequences’, Journal of Asian Public Policy 2(3): 266–78.

Barr

M.S.

Blank

R.M.

(2009) ‘Savings, Assets, Credit, and Banking among Low-income Households: Introduction and Overview’, in Blank

R.M.

Barr

M.S.

(eds) Insufficient Funds: Savings, Assets, Credit, and Banking among Low-income Households. New York: Russell Sage Foundation.

Chang

W.C.

Lim

L.L.

(2007) ‘Resilience and Well-being in an Asian Context – Singapore’, Journal of Psychology in Chinese Societies 8(1): 47–69.

Choo

L.S.

(2010) ‘An Overview of the Singapore Social Safety Net: Policies and Challenges’, in Rethinking Poverty and Social Mobility, pp. 5–16. Singapore: Civil Service College.

Conger

Elder

(1994) Families in Troubled Times: Adapting to Change in Rural America. New York: Aldine de Gruyter.

Dean

(2006) Social Policy. Cambridge: Polity Press.

Dew

(2007) ‘Two Sides of the Same Coin? The Differing Roles of Assets and Consumer Debt in Marriage’, Journal of Family and Economic Issues 28: 89–104.

Dew

(2008) ‘Debt Change and Marital Satisfaction Change in Recently Married Couples’, Family Relations 57: 60–71.

Farnsworth

Irving

(2011) ‘Driven by Economics, Determined by Politics: Social Policy in the Age of Austerity’, paper presented at the Annual Social Policy Association Conference, 4–6 July, University of Lincoln, UK.

10.

Gregory

Drakeford

(2006) ‘Social Work, Asset-based Welfare and the Child Trust Fund’, British Journal of Social Work 36: 149–57.

11.

Gudmunson

C.G.

Beutler

I.F.

Israelsen

C.L.

McCoy

J.K.

Hill

E.J.

(2007) ‘Linking Financial Strain to Marital Instability: Examining the Roles of Emotional Distress and Marital Interaction’, Journal of Family and Economic Issues 28: 357–76.

12.

Han

C.-K.

(2009) ‘Unemployment, Financial Hardship, and Saving in Individual Development Accounts’, Journal of Poverty 13(1): 74–95.

13.

Haveman

Wolff

E.N.

(2005) ‘Who are the Asset Poor? Levels, Trends, and Composition, 1983–1998’, in Sherraden

(ed.) Inclusion in the American Dream: Assets, Poverty, and Public Policy, pp. 61–86. New York: Oxford University Press.

14.

Institute of Policy Studies (IPS) (2009) ‘Resilience in the economic crisis: Findings of survey’ conducted 16 to 26 February 2009, National University of Singapore. Available online at: http://www.lkyspp.nus.edu.sg/ips/docs/events/pops/IPSPOPS(1)_PresentationWeb.pdf.

15.

Jang

S.J.

(2007) ‘Gender Differences in Strain, Negative Emotions, and Coping Behaviors: A General Strain Theory Approach’, Justice Quarterly 24(3): 523–53.

16.

Lazarus

(2004) ‘Relationships among Indicators of Child and Family Resilience and Adjustment following the September 11, 2001 Tragedy’, working paper no. 36, The Emory Center for Myth and Ritual in American Life.

17.

Lee

S. R.

Goh

C. B.

Fredriksen

Tan

J. P.

(2008) ‘Toward a Better Future: Education and Training for Economic Development in Singapore since 1965’, World Bank, Washington, DC.

18.

Lerman

R.I.

McKernan

S.-K.

(2008) ‘Benefits and Consequences of Holding Assets’, in McKernan

S.-K.

Sherraden

, Asset Building and Low-income Families, pp. 175–206. Washington, DC: The Urban Institute Press.

19.

Lipsky

(1980) Street-level Bureaucracy: Dilemmas of the Individual in Public Services. New York: Russell Sage Foundation.

20.

McCarthy

Mitchell

O.S.

Piggott

(2002) ‘Asset Rich and Cash Poor: Retirement Provision and Housing Policy in Singapore’, Journal of Pension Economics and Finance 1(3): 197–222.

21.

McCubbin

(2001) ‘Challenges to the Definition of Resilience’, paper presented at the 109th Annual Meeting of the American Psychological Association, 24–28 August, San Francisco, CA.

22.

McCubbin

H.I.

Patterson

(1983) ‘Family Transitions: Adaptation to Stress’, in McCubbin

H.I.

Figley

(eds) Stress and the Family: Volume 1. Coping with Normative Transitions, pp. 5–25. New York: Brunner Mazel.

23.

McCubbin

H.I.

Thompson

A.I.

McCubbin

M.A.

(2001) Family Measures: Stress, Coping and Resiliency. Honolulu, HI: Kamehameha Schools.

24.

McKernan

S.-M.

Sherraden

(2008) Asset Building and Low-income Families. Washington, DC: The Urban Institute Press.

25.

McLoyd

(1990) ‘The Impact of Economic Hardship on Black Families and Children: Psychological Distress, Parenting, and Socioeconomic Development’, Child Development 61: 311–46.

26.

Ministry of Manpower (2010) ‘Report on Labour Force in Singapore 2009: Statistical Tables’. Available online at: http://www.mom.gov.sg/statistics-publications/national-labour-market-information/publications/Pages/statistical-tables-2009.aspx (accessed 8 October 2010).

27.

Morillas

J.R.

(2007) ‘Assets, Earnings Mobility and the Black/White Gap’, Social Science Research 36: 808–33.

28.

Rothwell

D.W.

(2009) ‘A Rising Tide Lifts No Sunken Boat’, Social Space 2: 92–8.

29.

Parks-Yancy

DiTomaso

Post

(2007) ‘The Mitigating Effects of Social and Financial Capital Resources on Hardships’, Journal of Family and Economic Issues 28: 429–48.

30.

Rank

Hirschl

(2001) ‘Rags or Riches? Estimating the Probabilities of Poverty and Affluence across the Adult American Life Span’, Social Science Quarterly 82(4): 651–69.

31.

Rothwell

D.W.

Han

C.-K.

(2010) ‘Assets as a Resource Variable in the Stress Management of Low-income Families’, Family Relations 59: 396–407.

32.

Scanlon

Page-Adams

(2001) Effects of Asset Holding on Neighborhoods, Families, and Children: A Review of Research (CSD Research Report). St. Louis, MO: Washington University, Center for Social Development.

33.

Scott

Alwin

D.F.

(1989) ‘Gender Differences in Parental Strain: Parental Role or Gender Role?’, Journal of Family Issues 10: 482–503.

34.

Shapiro

T.M.

(2001) ‘The Importance of Assets’, in Shapiro

Wolff

E.N.

(eds) Assets for the Poor: Benefits and Mechanisms of Spreading Asset Ownership, pp. 11–33. New York: Russell Sage.

35.

Sherraden

(1991) ‘The Nature and Distribution of Assets’, in Assets and the Poor: A New American Welfare Policy, pp. 95–120. Armonk, NY: M.E. Sharpe.

36.

Sherraden

Laux

Kaufman

(2007) ‘Financial Education for Social Workers’, Journal of Community Practice 15(3): 9–36.

37.

Singapore Department of Statistics (2011) ‘Singapore in Figures’. Available online at: http://www.singstat.gov.sg/pubn/reference/sif2011.pdf (accessed 10 October 2011).

38.

Singapore Department of Statistics (2010) ‘Time Series on Per Capita GDP at Current Market Prices’. Available online at: http://www.singstat.gov.sg/stats/themes/economy/hist/gdp.html (accessed 8 October 2010).

39.

Solantaus

Leinonen

Punamäki

R.-L.

(2004) ‘Children’s Mental Health in Times of Economic Recession: Replication and Extension of the Family Economic Stress Model in Finland’, Developmental Psychology 40(3): 412–29.

40.

Tambyah

S.K.

Tan

S.J.

Kau

A.K.

(2009) ‘The quality of life in Singapore’, Social Indicators Research 92: 337–376.

41.

Tomlinson

Walker

(2010) Recurrent Poverty: The Impact of Family and Labour Market Changes. York: Joseph Rowntree Foundation (ISBN: 978 1 85935 731 6). Available online at: http://www.jrf.org.uk/sites/files/jrf/recurrent-poverty-impacts-full.pdf (accessed 12 December 2011).

42.

Vasoo

Lee

(2006) ‘Promoting Social Development through Integration of the Central Provident Fund and Public Housing Schemes in Singapore’, Social Development Issues 28(2): 71–83.

43.

Zhan

Sherraden

(2003) ‘Assets, Expectations, and Children’s Educational Achievement in Female-headed Households’, Social Services Review 77(2): 191–211.