CHAPTER SIX: GENDER ISSUE IN INCOME POVERTY AND WELL-BEING AMONG VULNERABLE GROUP IN MALAYSIA BY SEX DIFFERENCES
Nor Fairani Ahmad & Laily Paim
6.0 Introduction
The process of economic transformation has had social consequences, leading to an increase in poverty rates among vulnerable groups such as the elderly, single mothers, and the disabled. Various studies have examined the relationship between income poverty and well-being and have found that a low income does not always correspond to a lack of human well-being, and conversely, a high income does not guarantee high levels of well-being. Therefore, measuring poverty solely based on income or purchasing power does not necessarily reflect the level of well-being. This chapter focuses on exploring the sex differences among households with vulnerable members. The objectives are to investigate the relationship between income poverty and well-being and to determine the impact of socioeconomic variables in explaining well-being.
Data for this chapter is derived from an Official Poverty Line Survey conducted in four Malaysian cities, representing different regions in Peninsular Malaysia. The sample consists of 281 vulnerable households, conveniently selected, with 202 headed by males and 79 headed by females. Descriptive statistics, such as mean, median, and standard deviation, as well as multiple stepwise regression analysis, are utilised to examine the relationship between independent and dependent variables. The results from the regression analyses demonstrate significant differences in the contribution of socioeconomic variables to well-being based on sex. It is evident that sex differences in well-being exist, but the direction and magnitude of these differences are influenced by social and cultural backgrounds. These findings will be valuable in assisting government and policymakers in prioritising plans and policies for designing poverty eradication programs that aim not only to alleviate income poverty but also to improve overall life satisfaction.
Although the incidence of poverty in Malaysia has declined from 52.4 percent in 1970 to 3.8 percent in 2009, new forms of poverty have emerged, and inequalities, particularly among different sexes, persist. The dynamic nature of economic transformation has led to an increase in poverty rates, particularly affecting vulnerable groups such as the elderly, single mothers, and the disabled. Therefore, strategies for poverty eradication must be gender-sensitive, pro-poor, participatory, and tailored to address the diverse nature of poverty groups, as highlighted by Nair (2003).
Different approaches have been employed to study and understand poverty, including indicators such as income, consumption, material ownership, household durables, and economic well-being. Studies focusing on well-being as a means to understand poverty have revealed that income poverty alone does not adequately capture the multifaceted nature of poverty experienced by individuals. It gives focus on the gender issues by sex differences, among households that have vulnerable members. The objectives for this Chapter are,
to investigate the relationship between income poverty and well-being by sex, and
to determine the effects of socio-economic variables in explaining well-being by sex.
6.1 Literature Review
6.1.1 Income Poverty versus Well-Being
Poverty refers to a situation where individuals or families lack sufficient resources to meet their basic needs, including food, clothing, housing, and healthcare (Laily & Low-Income, 2010). In the context of income poverty, a household is considered to be in poverty if its income falls below a predetermined income threshold (Rojas, 2008). The World Bank defines poverty as a state of significant deprivation of well-being. According to the economic perspective on well-being, higher levels of income are generally associated with higher levels of well-being (Fuentes & Rojas, 2001). As income increases, more needs can be met, leading to an improved standard of well-being. In a study comparing income and well-being, Bleiweis, Diana, and Alexandra (2020) highlight that woman, especially women of color in the United States, are more likely to experience poverty than men, emphasising the need for targeted and comprehensive solutions to ensure their long-term economic security. However, Rojas (2005) found that there are discrepancies between the information provided by objective socioeconomic indicators and one’s perception of their socioeconomic condition. There is no obvious indication that poor households as categorised by the objective definition recognises themselves as poor, whilst the households that feel poor are classified as being non-poor by the objective measure.
6.1.2 Vulnerable Group, Income Poverty and Well-Being
Rojas (2008) in Mexico examined the incidence of income poverty and experiential poverty (assessed through life satisfaction). The study found that 11.5% of individuals were income-poor but not experientially poor, while 11.2% were non-income-poor but experientially poor. This suggests that the experience of poverty can differ from the mere absence or presence of income poverty.
Focusing on disabled individuals in Canada, Uppal (2006) observed that happiness or well-being was negatively correlated with the severity of disability. Interestingly, per capita family income did not have a significant effect on happiness or well-being.
These studies highlight that well-being and poverty cannot be solely determined by income levels. Factors such as personal experiences, life satisfaction, and individual circumstances play crucial roles in shaping one's well-being, irrespective of income status.
6.1.3 Gender Perspectives in Income Poverty and Well-Being
Households headed by female have been found to be among the poorest of the poor because they are likely to be economically disadvantaged (UNESCAP, 2000). According to the BRIGDE report (2001) there is a concerning trend of increasing poverty among women, particularly in relation to the rising rates of female-headed households. In 2002, rural households headed by the elderly and female recorded an incidence of poverty of 28.6% and 25.7% respectively (EPU Malaysia, 2003).
Research suggests that there is a tendency for men to report slightly higher levels of happiness compared to women. However, the evidence regarding gender differences in happiness is not entirely consistent. According to Annette Svanberg-Miller (2004), studies conducted in Australia have identified gender differences in subjective well-being, but these differences are generally small in magnitude. The specific nature of these differences is influenced by various factors, including social and cultural backgrounds, as well as how well-being is perceived and understood within each gender. It's important to recognise that the extent and direction of gender differences in happiness may vary across different societies and contexts.
6.2 Methodology
6.2.1 The Data and Instrument
The data for this study was obtained from an Official Poverty Line Survey (OPLS), which was funded by the Fundamental Research Grant Scheme (FRGS) and conducted in the year 2008. The survey was carried out in four Malaysian cities, representing each region in Peninsular Malaysia. The sampling technique employed in the survey was multistage random sampling, which involved selecting areas at regional, state, district, and sub-district levels. The samples were conveniently selected for participation in the survey.
The data collection process involved the use of a questionnaire comprising various sections, covering aspects such as the head of the household and family profile, social and economic background, socio-economic status perception, and well-being. A total of 604 completed questionnaire forms were collected.
However, for the purposes of this particular chapter, only 281 respondents were included in the analysis. The sample selection was based on households that met at least one of the following criteria: being income poor, having an elderly person, being headed by a single mother, and/or having a disabled person. Out of the 281 vulnerable households, 202 were headed by males, while 79 were headed by females.
6.2.2 Variables Definition Measurement
6.2.2.1 Dependent Variable
Well-being (WB) is the dependent variable with 16-items question using overall life satisfaction scale. These questions were based on a scale of 1-10, where 1 means ‘completely dissatisfied’ and 10 means ‘completely satisfied’. The questions asked were: ‘On a scale of 1 to 10, how satisfied are you with the following areas in your life now?’ The composite score for overall well-being, calculated based on the responses from the respondents, is considered a continuous variable.
6.2.2.2 Independent Variable
Socio-demographic variables in this Chapter are stratum, sex, ethnic, age, marital status, education, household size, house ownership, employment status, and perceived health feeling (PHF). Age, household size and PHF are treated as continuous and the rest of the variables are in categorical. Stratum refers to urban (1) and rural (0), while sex would be male (1) and female (2). Ethnicity refers to Malay, Chinese, Indian and others, and is treated as dummy variables. Marital status is categorised as widowed/divorced (1) and married (0). For educational attainment, it is categorised into three groups: no formal schooling, primary school and secondary and higher. This variable is later treated as dummy variables. Job status has three categories which are active in employment (AIE), active self-employed (ASE) and out of employment (OOE), and is treated as dummy variables. The house ownership is divided into three groups: owner, rented and others, and also is treated as dummy variables. Perceived health feeling (PHF) refers to the subjective evaluation of given health situations. It is measured as a mean of score of 12-items questions asked related to the frequency of feeling felt whether never, rarely, sometimes, frequent or always.
Economic variables are,
i. household percapita income that is calculated by dividing the total household income with household size, and
ii. perception of socio-economic status (PSES) which include questions on poverty perception, perception on economic well-being and perception on satisfaction of material needs of respondent’s current household situation. A total score for the three statements is calculated to get the score for PSES. Both economic variables are measured as continuous variables.
6.2.3 Data Analysis
The data analysis for this study is conducted using the SPSS version 16.0 program. Descriptive statistics, such as frequencies, percentages, mean, median, and standard deviation, are employed to examine the distribution of respondents in relation to their socio-economic factors. These descriptive statistics provide a summary of the characteristics and variation in the data.
In addition to descriptive statistics, several statistical techniques are utilised to explore the relationships between independent and dependent variables. The Chi-Square test is employed to examine the associations between categorical variables. The Pearson product-moment correlation is utilised to assess the strength and direction of the linear relationship between two continuous variables.
Furthermore, multiple stepwise regression analysis is conducted to predict the relationship between independent variables and the dependent variable. This regression analysis allows for the identification of significant predictors and the estimation of their impact on the dependent variable. The stepwise approach helps in selecting the most relevant predictors based on their statistical significance.
By employing these statistical methods, the study aims to uncover patterns, associations, and predictive factors related to the variables of interest and provide a deeper understanding of the relationships between socio-economic factors and overall well-being.
6.3 Results and Discussion
6.3.1 Profile Respondents
A total of 281 vulnerable households were analysed for this Chapter, 202 males headed (MHH) and 79 females headed (FHH). For MHH, about 47% were income poor (IP), 33% were elderly (EP) and about 18% were both (IP+EP). As for the FHH, single mother (SM), EP, both (EP+SM) and SM+disabled person (DP) has the same percentage, about 20%, then, followed by SM+IP (19%), and IP (9%).
Over two-third of MHH and FHH lived in rural areas (Table 1). Malay MHH (76.2%) and FHH (83.5%) were the highest ethnic in this Chapter, followed by Indian and Chinese. The proportion for married MHH was higher (94%), whilst widowed/divorced FHH was 88.23%. The findings for crosstab analysis for marital status by sex was found to be significantly different [Pearson c2 (1, N = 278) = 0.018, p = 0.012, Phi = 0.814], the pattern of relationships revealed that FHH had more proportion of widowed/divorced compares to MHH. For the education background, primary schooled (47.5%) was the highest for MHH, whilst FHH, the proportion was almost equal for three categories (no formal schooling, primary & secondary and higher). The findings for crosstab analysis for education background by sex was found to be significantly different [Pearson c2 (2, N = 277) = 23.39, p = 0.0001, Contingency Coef. = 0.279], the pattern of relationships revealed that MHH were more educated than FHH. About 42% MHH were still active in employment, compared to about two-third (66%) of FHH were out of employment, MHH were 2 times more likely to be in employment than FHH [Pearson c2 (2, N = 281) = 32.84, p = 0.0001, Contingency Coef. = 0.342]. As for house ownership, the majority of MHH and FHH were owners with about 68% and 81% respectively.
The mean age for both MHH and FHH were about the same. As for the size of the household, there is a significant difference between MHH and FHH with t(DF=279) = 3.607, p < 0.01, with MHH (mean = 5) having a higher mean compared to FHH (mean = 4). Mean household income for MHH (RM873.45) was higher than FHH (RM748.14). This happened because 66% of FHH were out of employment compared to MHH (42%) who were still employed. MHH scored higher for perceived health feeling (PHF) compared to FHH. As for the perceived socio-economic status (PSES), MHH scored a higher mean compared to FHH.
Table 1: Socio-demographic Profile by Sex
Socio-Demographic Profile | Overall | Male HH | Female HH | t-Test / Chi-Sqr | |
Freq (%) | Freq (%) | Freq (%) | p-value | ||
Stratum | Urban Rural | (n=281) | (n=202) | (n=79) |
|
93 (33.1) 188 (66.9) | 66 (32.7) 136 (67.3) | 27 (34.2) 52 (65.8) | 0.810 | ||
Ethnic | Malay Chinese Indian Others | 220 (78.3) 27 (9.6) 32 (11.4) 2 (0.7) | 154 (76.2) 21 (10.4) 25 (12.4) 2 (1.0) | 66 (83.5) 6 (7.6) 7 (8.9) - | 0.522 |
Marital Status | Married Widowed/Divorced | (n=278) | (n=201) | (n=77) | 0.0001 c2 = 0.018, DF = 1 Phi = 0.814 |
198 (71.2) 80 (28.8) | 189 (94.0) 12 (6.0) | 9 (11.7) 68 (88.3) | |||
Education Background | No formal Primary (1 - 6 yrs) Secondary &abv (³7yrs) | (n=277) | (n=202) | (n=75) | 0.0001 c2 = 23.39, DF = 2 Contingency Coef.= 0.279 |
39 (14.1) 123 (44.4) 115 (41.5) | 16 (7.9) 96 (47.5) 90 (44.6) | 23 (30.7) 27 (36.0) 25 (33.3) | |||
Job Status | Active in Employment (AIE) Active Self Employed (ASE) Out of Employment (OOE) | 100 (35.6) 71 (25.3) 110 (39.1) | 84 (41.6) 60 (29.7) 58 (28.7) | 16 (20.3) 11 (13.9) 52 (65.8) | 0.0001 c2 = 32.84, DF = 2 Contingency Coef.= 0.342 |
House Ownership | Owner Rented Others | 261 (71.5) 23 (8.2) 57 (20.3) | 137 (67.8) 17 (8.4) 48 (23.8) | 64 (81.0) 6 (7.6) 9 (11.4) | 0.057 |
Age Head of HH | (n=279) | (n=200) | (n=79) | 0.833 | |
57.48 ± 13.21 | 57.38 ± 13.33 | 57.75 ± 13.01 | |||
HH Size | 4.73 ± 2.24 | 5.02 ±2.10 | 3.97 ± 2.42 | 0.0001 t-stat = 3.607 DF = 279 | |
HH Income | 838.22 ± 610.37 | 873.45±576.58 | 748.14±685.01 | 0.122 | |
Perceived Health Feeling (PHF) Mean Score (1 – 5) | 3.82 ± 0.59 | 3.85 ± 0.62 | 3.72 ± 0.49 | 0.095 | |
Perceived Socio-economic Status (PSES) Sum Score (3 – 12) | 8.14 ± 1.76 | 8.25 ± 1.79 | 7.84 ± 1.67 | 0.075 | |
6.3.2 Well-Being Distribution
Table 2 shows the distribution of well-being (WB) in 10 equal groups (deciles). Overall, 41.6% in bottom 40 percent (mean=5.25), 38.4% in middle 40 percent (mean=6.89) and 19.9% in top 20 percent (mean=8.41). 44.1% MHH were in the bottom 40 percent, followed by middle 40 percent (36.6%) and top 20 percent (19.3%). For FHH, the highest was middle 40 percent (43%), followed by bottom 40 percent (35.4%) and top 20 percent (21.5%). The means WB for FHH in bottom 40 is slightly lower compared to MHH. For middle 40 percent is the same for both. However, for top 20 percent, mean WB for FHH is slightly higher than MHH. These findings were almost in line with earlier study conducted by Annette Svanberg-Miller (2004).
A chi-square test of independence was conducted to assess whether the WB (bottom 40%, middle 40% and top 20%) was related to the sex. The finding of crosstabs analysis for both was found to be significantly nodifferent [Pearson c2 (2, N = 281) = 1.748, p = 0.415].
Table 2: Sample Distribution across Deciles for Well-Being (WB) by Sex
| Decile | Male HH |
| Female HH |
| Overall | ||||
Frequency | n / % within | Frequency | n / % within | Frequency | n / % of Total | |||||
Bottom 40% | 1 | 20 | 89 / 44.1% Mean = 5.31 S.D. = 0.92 |
| 10 | 28 / 35.4% Mean = 5.06 S.D. = 1.11 |
| 30 | 117 / 41.6% Mean = 5.25 S.D. = 0.97 | |
2 | 23 | 5 | 28 | |||||||
3 | 23 | 6 | 29 | |||||||
4 | 23 | 7 | 30 | |||||||
Middle 40% | 5 | 20 | 74 / 36.6% Mean = 6.89 S.D. = 0.40 |
| 7 | 34 / 43.0% Mean = 6.89 S.D. = 0.39 |
| 27 | 108 / 38.4% Mean = 6.89 S.D. = 0.40 | |
6 | 17 | 10 | 27 | |||||||
7 | 18 | 9 | 27 | |||||||
8 | 19 | 8 | 27 | |||||||
Top 20% | 9 | 22 | 339 / 19.3% Mean = 8.37 S.D. = 0.61 |
| 7 | 17 / 21.5% Mean = 8.50 S.D. = 0.52 |
| 29 | 56 / 19.9% Mean = 8.41 S.D. = 0.58 | |
10 | 17 | 10 | 27 | |||||||
| Total | 202 | 71.9% |
| 79 | 28.1% |
| 281 | 100% | |
| Pearsonc2 = 1.748 DF = 2 p-value = 0.415 Contingency Coef. = 0.079 a. 0 cells (.0%) have expected count less than 5. The minimum expected count is 15.74. | |||||||||
6.3.3 Household Income and Well-Being
Table 3 provides a comparison of well-being (WB) across different household income (HHI) groups and also shows a relationship between both variables by sex. Overall, as we move from lower to higher HHIgroup more households are located in the bottom and middle 40 percent categories. About 34% of the total households who are in the bottom 40 percent of HHI are in bottom and middle 40 percent of WB, while for those household who are in the top 20 percent HHI, only 7.5% in the top 20 percent WB. However, there still exist households in the bottom 40 percent of HHI who have high WB (6.8%), also, household in the top 20 percent of HHI who have low WB (5.0%).
As for FHH, most of the households are in the bottom and middle 40 percent between both variables. 45.6% of the total households who are in the bottom 40 percent of HHI are in bottom and middle 40 percent of WB, while for those household who are in the top 20 percent HHI, only 8.9% in the top 20 percent WB. However, there still exist households in the bottom 40 percent of HHI who have high WB (8.9%), also, household in the top 20 percent of HHI who have low WB (2.5%).
Meanwhile, the trend for MHH is a bit different compared to FHH. Most of the households are in the middle 40 percent of HHI and both bottom and middle 40 percent of WB. About 35.1% of the total households who are in the middle 40 percent of HHI are in bottom and middle 40 percent of WB, while for those household who are in the top 20 percent HHI, only 6.9% in the top 20 percent WB. However, there still exist households in the bottom 40 percent of HHI who have high WB (5.9%), also, household in the top 20 percent of HHI who have low WB (5.9%).
A chi-square test of independence was conducted to assess whether the HHIcategories is related to the different level of WB by sex differences. The findings from crosstabs analyses for all groups are found to be significantly different and we can conclude that level of well-being is significantly dependent on household income’s categories.
Table 3: Household Income (HHI) and Well-Being (WB) in % of Total by Sex
HH Income | Well-Being | ||||||||||||
Male Headed |
| Female Headed |
| Overall | |||||||||
Bottom 40% | Middle 40% | Top 20% | Bottom 40% | Middle 40% | Top 20% | Bottom 40% | Middle 40% | Top 20% | |||||
Bottom 40% | 20.8 | 8.9 | 5.9 |
| 20.3 | 25.3 | 8.9 |
| 20.6 | 13.5 | 6.8 | ||
Middle 40% | 17.3 | 17.8 | 6.4 |
| 12.7 | 16.5 | 3.8 |
| 16.0 | 17.4 | 5.7 | ||
Top 20% | 5.9 | 9.9 | 6.9 |
| 2.5 | 1.3 | 8.9 |
| 5.0 | 7.5 | 7.5 | ||
Total | 100 |
| 100 |
| 100 | ||||||||
Pearsonc2 | 14.80 |
| 16.40 |
| 18.77 | ||||||||
DF | 4 |
| 4 |
| 4 | ||||||||
p-value | 0.005 |
| 0.003 |
| 0.001 | ||||||||
Contingency Coef. | 0.261 |
| 0.415 |
| 0.250 | ||||||||
6.3.4 Factors Affecting Well-Being by Sex
Table 4 is the results of the regression analysis performed between the socio-demographic and economic variables on WB by sex differences using correlation and multiple regression analysis on all variables simultaneously. For MHH, six independent variables are significantly correlated; Malays (+), Chinese (-), Indian (-), HHPCI (+), PSES (+), and PHF (+). There are relatively negligible and moderate relationships between these variables and WB with a Pearson correlation between 0.162 to 0.490. As for FHH, there are only five independent variables that are significantly correlated; widowed/divorced (-), Malay (+), Chinese (-), owned house (+), and HHPCI (+). There are relatively negligible and moderate relationships between these variables and WB with Pearson correlation between 0.069 to 0.484.
Table 4: Explanatory Power of Socio-Demographic and Socio-Economic Indicators on Well-Being by Sex– Correlation and Multiple Stepwise Regression Analysis
Independent Variables | Male Headed HH |
| Female Headed HH | |||||||
Pearson Corr. | Unstd. Coef. | Std. Coef. | Sig. value |
| Pearson Corr. | Unstd. Coef. | Std. Coef. | Sig. value | ||
| R | B | Beta | r | B | Beta | ||||
(Constant) |
| 2.052 |
|
|
|
| 6.481 |
|
| |
Socio-demographic Indicators: |
|
|
|
|
|
|
|
| ||
Stratum (Urban=1) | 0.077 | -0.505 | -0.174 | 0.007 |
| 0.013 | n.s. | n.s. | n.s. | |
HHH Age (yrs) | 0.074 | n.s. | n.s. | n.s. |
| -0.122 | n.s. | n.s. | n.s. | |
Marital Status: (Widow/Divorced=1) | -0.048 | n.s. | n.s. | n.s. | -0.306** | -1.233 | -0.288 | 0.002 | ||
Ethnic: (Malays=1) | 0.261** | n.s. | n.s. | n.s. | 0.332** | n.s. | n.s. | n.s. | ||
(Chinese=1) | -0.225** | -0.921 | -0.207 | 0.001 | -0.484** | -2.480 | -0.444 | 0.000 | ||
(Indian=1) | -0.162* | n.s. | n.s. | n.s. | 0.017 | n.s. | n.s. | n.s. | ||
HHH Job Status: Active in Empl. (AIE=1) | 0.012 | n.s. | n.s. | n.s. | -0.103 | -0.699 | -0.190 | 0.039 | ||
Active Self Empl. (ASE=1) | 0.016 | n.s. | n.s. | n.s. | -0.055 | n.s. | n.s. | n.s. | ||
HHH Education (Sec.&above=1) | 0.063 | n.s. | n.s. | n.s. | 0.109 | n.s. | n.s. | n.s. | ||
Size of HH (#) | -0.067 | n.s. | n.s. | n.s. | 0.136 | n.s. | n.s. | n.s. | ||
House Ownership: (Own=1) | 0.113 | n.s. | n.s. | n.s. | 0.207** | n.s. | n.s. | n.s. | ||
(Rented-1) | 0.099 | 0.576 | 0.118 | 0.046 | -0.152 | n.s. | n.s. | n.s. | ||
Socio-economic Indicators: |
|
|
|
|
|
|
|
| ||
Per Capita Income (HHPCI) | 0.284** | 0.001 | 0.153 | 0.014 | 0.069** | n.s. | n.s. | n.s. | ||
Perceived Socio-economic Status (PSES) | 0.490** | 0.284 | 0.371 | 0.000 | 0.421 | 0.191 | 0.214 | 0.031 | ||
Perceived Health Feeling (PHF) | 0.413** | 0.541 | 0.247 | 0.000 | 0.095 | n.s. | n.s. | n.s. | ||
R / R Square / Adj. R Square | 0.610 / 0.372 / 0.353 |
|
| 0.642 / 0.412 / 0.381 | ||||||
F Statistic / Sig. value | 19.058 / 0.0001 |
| / 0.0001 | |||||||
* Correlation is significant at the 0.05 level (2-tailed)
** Correlation is significant at the 0.01 level (2-tailed)
Multiple stepwise regression analysis performs by sex results in few indicators being significant. Result shows that for MHH, there are six (6) indicators that are significant, with explanatory power of about 37%. Well-being of vulnerable household who headed by male is positively affected by PSES, PHF, HHPCI and those who lived in rented house, and negatively affected by those lived in urban and ethnic Chinese. The largest beta coefficient is 0.371 (PSES), follow by 0.247 (PHF), 0.207 (Chinese), 0.174 (Urban), 0.153 (HHPCI) and 0.118 (Rented),
Again, when multiple stepwise analyses are performed on FHH, there are only four (4) indicators that are significant, with explanatory power of about 41%, higher than MHH. Well-being of vulnerable household who headed by female is positively affected by PSES only and negatively affected by widowed/divorced FHH, ethnic Chinese and those who are employed. The largest beta coefficient is 0.444 (Chinese), follow by 0.288 (widowed/divorced), 0.214 (PSES) and 0.190 (AIE).
6.4 Conclusion
The findings from this Chapter would enhance our understanding of WB by examining its’ relationship with income poverty and the determinants of WBusing the socio-economic variables, with focus on the gender issues by sex among vulnerable households.
Firstly, from the chi-square tests performed on the socio-demographic variables by sex, we can conclude that marital status, education background and job status are significantly dependent on sex with low to high relationship. Also, the size of household headed by male is significantly higher than the female. Secondly, we can conclude that overall, majority of the vulnerable household are moderately satisfied with their well-being. Also, from the chi-square test conducted, there is no significant dependency in level of well-being on sex. Next, the level of well-being is significantly dependent on household income’s categories for both male and female. The association between both variables is higher for FHH (0.415) compared to MHH (0.261). The observation that there are individuals who are considered poor based on relative income poverty measures but do not perceive themselves as such, while others who perceive themselves as poor have higher incomes by objective measures, highlights the complexity of poverty and well-being assessments.
Finally, findings on the relationship between WB and socio-demographic and economic variables using Pearson product-moment correlation coefficients show that all indicators have relatively negligible to moderate with mixed (positive and negative) relationship with WB for both sexes. The significantly correlated indicators are Malays (+), Chinese (-), Indian (-), HHPCI (+), PSES (+) and PHF (+) for MHH, and widowed/divorced (-), Malay (+), Chinese (-), owned house (+) and HHPCI (+) for FHH. Further investigation to identify the best set of socioeconomic indicators that would predict WB, finds that six (6) indicators are significant for MHH, which are PSES (+), PHF (+), HHPCI (+), rented house (+), live in urban (-) and ethnic Chinese (-) with an explanatory power of 37% variance in WB. For FHH, four (4) indicators are significant which are PSES (+), widowed/divorced (-), ethnic Chinese (-) and those who are employed (-) with an explanatory power of 41% variance in WB. Multiple regressions show large differences in the contribution of socio-economic variables to well-being by sex, indicating that sex differences in well-being do exist. However, the direction and effect size of these differences appears to be affected by social and culture background.
These findings hold significance for government officials and policymakers as they aid in determining priorities and developing policies for poverty eradication programs. The focus should extend beyond solely lifting individuals out of income poverty and also strive to create conditions that lead to a satisfying and fulfilling life, particularly for vulnerable households. By considering the multifaceted nature of poverty and addressing the various factors that contribute to well-being, policymakers can design more effective strategies to improve the lives of those experiencing poverty.
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