1. Introduction
The issues of poverty and vulnerability have long dominated policy-making. The extensive literature on development has provided broad and deep discussions on these two persistent areas of concern against the backdrop of sustainable development, of which proposed solutions are abundant. The concept of vulnerability—the risk of experiencing poverty in the future—has recently taken center stage following successive international economic shocks such as the global economic crisis, which had increased the emergence of poverty and hardcore poverty, if not, intensified it [
1]. Despite tireless efforts to reduce the incidence of poverty and hardcore poverty [
2], the socio-economic vulnerability among the poor in developing countries, not limited to the low-income households in Malaysia [
3], remains a threat. According to Zarina and Kamil [
4], government policies and economic systems determine the success or failure of the very system that addresses the level of economic vulnerability in the society. Despite the government’s primary concern and priority in the reduction of vulnerability and its corresponding issues on society, poverty and vulnerability persist regardless of the countless efforts of policymakers.
The Malaysian policy development scene continues under the Tenth Malaysia Plan (2011–2015) and the National Transformation Policy (NTP), through the New Economic Model. These policies set the goal of moving the country towards a high-income economy. This transformation is supported by the Economic Transformation Programs and Government Transformation Programs, both of which focus on eradicating poverty, decreasing the level of economic vulnerability, and improving the living standards of Malaysians. Thus, the government formulated various entrepreneurship development initiatives where the key emphasis is on efforts to enhancing income-earning opportunities to increase the income and reduce the level of economic vulnerability, which when achieved, can lead to an improvement in the standard of living among low-income households [
5]. In the formulation of entrepreneurship development initiatives, two primary instruments are used to generate income: micro-credit and enterprise development training programs. To deliver these assistances, the government established the development organizations of Amanah Ikhtiar Malaysia (AIM), the National Entrepreneurs Economic Group Fund (TEKUN), and the Malaysia Fisheries Development Board (LKIM).
During the conception years, AIM, TEKUN, and LKIM only offered micro-credit for the purposes of creating small businesses and engagement in other economic activities, but today the mechanism is dominated by a two-pronged strategy. The first is micro-credit for business working capital and the second is training, specifically for the development of entrepreneurial skills. The three said organizations offer a diverse range of micro-credit to low-income households in the Kelantan state. AIM provides three types of economic loans: a recovery loan, an education loan, and a housing or multipurpose loan. Meanwhile, TEKUN, which is an agency under the Ministry of Entrepreneurial and Cooperative Development, provides four types of economic loans and financing. Lastly, LKIM, an authorized body under the Ministry of Agriculture and Agro-Based Industry, provides several small-scale loans for working capital, ship, and fishing equipment upgrades to low-income households in the fisheries industry.
In terms of entrepreneurial development training programs, AIM provides three major programs to enhance business understanding and skills, as well as to learn how to monitor risk management plans. As for TEKUN, only one training program is provided with the objective to train new borrowers about the basics of marketing and accounting. Meanwhile, LKIM provides four types of micro-enterprise training for fishermen and their households: community development, institutional development, general assistance, and poverty eradication.
These initiatives have been considered as the most important and effective mechanism in boosting household income [
6,
7,
8], micro-enterprise income [
9,
10], and micro-enterprise asset net worth [
9,
11] that leads to the eventual decrease in economic vulnerability [
12,
13]. To reiterate, the aim of this study is to examine the effect of economic vulnerability on participation in development programs, household income, micro-enterprise income, and assets among the participants of various development initiatives in the state of Kelantan. This study chose Kelantan state as it remains the poorest state in Peninsular Malaysia, achieving only a 0.4% poverty rate in 2017 and the lowest mean monthly household income of RM4214 [
14]. In an attempt at determining the effect of economic vulnerability, this study adopted a cross-sectional design and gathered quantitative data from the participants of development organizations operated in Kelantan, Malaysia. The outcomes of this study are deemed to enhance the overall knowledge and the understanding among development policy makers, academicians, development organizations, and low-income vulnerable households in Malaysia, which are expected to devise more inclusive and diversified development policies and programs towards improvising the socio-economic impact of development initiatives in Malaysia.
The structure of this study is as follows. Section one, the introduction, sets the scene discusses the poverty and vulnerability issues and the government’s plan and strategies to eradicate the poverty. Section two, the core of the study, lays out our theoretical background and discussed the past studies: it the concept of vulnerability we think is useful to examine important development programs in light of the crisis. In this section, we discussed possible ways of dealing with vulnerability into four variables-participations in development programs, households income, micro-enterprise income and micro-enterprise assets- and linked them to the earlier defined concept of exposure. It is contended that the economic vulnerability has positive as well as negative effects on these four variables. Section three, the research methodology part, discusses the study design and determines the sample size, the operational definition and the control variable used in this study. Section four is the summary of the findings, followed by a discussion. Section six, which is the last section of this study, is the conclusions, limitations of the study, and the recommendations for future researchers.
3. Research Methodology
This study employed the cross-sectional design and collected quantitative data through structured interviews. The population of this study refers to a total of 88,435 low-income households identified as participants of development programs initiated by AIM, TEKUN, and LKIM in Kelantan, Malaysia. The research team approached the said development organizations for a list of at least 150 participants, each with their name, address, and contact details. AIM, TEKUN, and LKIM provided lists of 500, 350, and 156 randomly selected existing participants of their programs. The listed participants (1006 participants) derived from seven districts, included Tumpat, Bachok, Pasir Puteh, Pasir Mas, Tanah Merah, Gua Musang, and Jeli. Next, the research team communicated with listed participants to explain the purpose of the survey and to arrange for an appointment with them. Then, the research team visited the respondents place between 8 November 2017 and 31 December 2017 and of the 1006 listed participants; this study secured the participation of 450 respondents (AIM-150, TEKUN-150, LKIM-150). Data were collected from the respondents via structured face-to-face interviews conducted at their preferred location.
3.1. Sample Size
This study employed Krejcie and Morgan’s [
29] guidelines formula to determine the sample size as follows:
where,
s = the required sample size;
= the table value of chi-square for 1 degree of freedom at the desired confidence level (3.841);
N = the population size (88,435);
P = the population proportion (assumed to be 0.50); and
d = the degree of accuracy expressed as a proportion (0.05).
As per Krejcie and Morgan [
29], a sample size of 383 was required for a population of 88,435. To address the possible complications with regards to the small sample size, this study ensured the collection of data from 450 respondents.
3.2. Operational Definitions
The length of participation is defined as the duration spent by the respondents participating in the programs under the AIM, TEKUN, and LKIM development initiatives. The total amount of economic loan received refers to the amount of credit that respondents had obtained from AIM, TEKUN, and LKIM. The hours of enterprise development training refers to the total number of enterprise development training programs attended, the total number of program training hours attended, and the total number of weekly meeting and/or discussions attended in the last 12 months.
Household income refers to the average monthly income obtained from all sources by all household members in the last twelve months. Micro-enterprise income refers to ‘the changes in the average monthly income’ and the net worth of microenterprise assets refers to ‘the sum of the approximate market value of all assets used in the enterprise including vehicles, machinery, raw materials and finished goods’.
The definition of economic vulnerability is the exposure of microenterprises to potentially harmful external economic events. Other studies had conceptualized economic vulnerability as vulnerability to income and asset poverty resultant from exposures to topological, natural and economic disasters. In this study, economic vulnerability is measured by using the index, adopted from Al-Mamun, Mazumder, and Malarvizhi [
12].
where,
= the vulnerability index that measures the level of economic vulnerability among the participating households;
= the coefficient variation for the average monthly income earned (last twelve months) among the three groups of households based on their business period;
where,
represents the average microenterprise asset net worth within the same group of respondents, while
reflects the net-worth of the enterprise assets;
= the proportion of the total income from the micro-enterprise income (business owned and managed by the respondent);
where,
is the effect of the poverty level upon economic vulnerability measured, while
refers to the average monthly income for households. Whereas
denotes the income of the bottom 40% of the population in Malaysia, amounting to RM2848 per household per month (RM: Ringgit Malaysia) (DOSM, 2017);
measures the effect of diversion in income sources due to an economic vulnerability where
is the total number of income sources (full-time); and
= the households with the proportion of dependent members per gainfully employed member.
3.3. Control Variables
Several variables such as age, gender [
22,
23,
24,
25,
26,
27,
28,
31], marital status [
32] and education [
31] are discovered to impact household income, micro-enterprise income, micro-enterprise asset net worth, and economic vulnerability. To elaborate, in terms of age, older participants are more skilled and experienced, hence, able to secure higher income and assets compared to younger participants. In terms of gender, women’s participation in income generating activities is less common in developing countries attenuated by social and religious norms and practices. Correspondingly, male household members are expected to secure higher income and experience less economic vulnerability than their female counterpart. Then, in terms of marital status, married participants are able to secure more income and assets compared to divorced and separated participants. Additionally, participants with a high level of education earn more and experience less economic vulnerability than those without.
Among the selected variables, this study assessed the effect of gender by assigning the value ‘1’ for male and ‘0’ for female, with the assumption that the borrowers’ gender has an effect on participation, household income, micro-enterprise income, and micro-enterprise asset net worth. As for marital status, this study assigned the value ‘1’ for married respondents and ‘0’ for single, widowed and divorced respondents, with the assumption that marital status has an effect on participation, household income, micro-enterprise income and net worth of the micro-enterprise asset.
3.4. Data Analysis
After data collection, the data were summarized and analyzed in an easy-to-understand form for interpretation and tabulation. The analysis was carried out by using the Statistical Package for Social sciences (SPSS). Multiple regression analysis was performed to establish the relationship between economic vulnerabilities with participation in development programs and the socio-economic well-being of the low-income households in Kelantan, Malaysia. Pearson correlation analysis was conducted to determine the relationships linked to controlling the effects of the selected antecedents. Typically, the presence of outliers ought to be high in such a study’s genre due to the relatively higher variation in the distribution. Hence, the model was tested for multivariate normality to discard outliers. After that, the model was re-tested to verify that the original findings and significances were not much affected by the absence of multivariate normality.
4. Summary of Findings
4.1. Demographic Characteristics
Of the data provided, 450 respondents (49.8%) were males while the remaining were females (see
Table 1). Most of the respondents (44.4%) were aged between 41 and 50 years old, 27.8% were aged between 51 and 60 years old, while 14.2% were aged between 31 and 40 years old, and 8.9% were aged 61 years old and above. The remaining age cohort was below 30 years old. As for marital status, 94% of respondents were married, 4.9% were single, 0.9% were widowed and only one (0.2%) was separated from his/her partner. Additionally, 42.7% of respondents ran their enterprise for 6 to 10 years, while 32.0% ran their enterprise for 11 to 15 years, 13.3% of them ran their enterprise for 16 to 20 years, and 11.6% of them ran their enterprise for 1 to 5 years. However, only 0.4% of the respondents have been operating their enterprise for more than 21 years. Industry-wise, the majority of the respondents (59.1%) were service providers, 17.8% were involved in retail, 11.5% were involved in manufacturing, and 7.3% were in fisheries. The remaining 3.8% and 0.4% were involved in livestock and wholesaling business respectively.
4.2. Descriptive Analysis
In
Table 2, the mean value for the economic vulnerability was 0.67% with a standard deviation of 0.58%. Participation duration in years in development initiatives was 10.87 years with a standard deviation of 4.43 years. Next, the mean value for the total number of training programs attended was 5.5 times with a standard deviation of 2.77 times. The total number of program training hours obtained had a mean value of 40.47 h with a standard deviation of 22.87 h. The mean value for the total amount of economic loan received was RM21,454.44 with a standard deviation of RM11,167.23. In addition, the mean value for the average monthly household income was RM1834.75 with a standard deviation of RM865.74. This was followed by the mean value for the average monthly micro-enterprise income as RM1604.31 with a standard deviation of RM812.37. Thus, the mean value for the micro-enterprise asset net worth was RM29,295.63 with a standard deviation of RM12,282.23. Further, the mean value for age was 48.31 years old with a standard deviation of 9.612 years old.
Table 3 presents the mean difference of a number of participation years, number of training programs attended, number of program training hours, the total amount of economic loan received, average monthly household income, average monthly micro-enterprise income, and micro-enterprise asset net worth among various groups according to the level of economic vulnerability of the respondents. The findings revealed that the mean number of participation years scored the highest among respondents with the highest level of economic vulnerability. As for the number of training programs attended, the findings revealed that participants who received a significant training experience were relatively less vulnerable to economic shocks than that of others. Similarly, the higher the number of program training hours attended saw respondents experiencing lower levels of economic vulnerability than that of others. Another finding showed that the cohort with lower levels of economic vulnerability received higher amounts of an economic loan from development organizations. Moreover, respondents with relatively higher levels of economic vulnerability attained relatively lower average monthly household income, average monthly micro-enterprise income, and micro-enterprise asset net worth, than that of others.
4.3. Partial Correlation
A partial correlation was performed to determine the relationship between economic vulnerability, the participation indicators, average monthly household income, average monthly micro-enterprise income, and the micro-enterprise asset net worth.
Table 4 reported that economic vulnerability had a positive correlation with the number of participation years (
p-value = 0.003), number of training programs attended (
p-value = 0.025), number of program training hours (
p-value = 0.000), the total amount of economic loan (
p-value = 0.000), the average monthly household income (
p-value = 0.000), the average monthly micro-enterprise income (
p-value = 0.000), and micro-enterprise asset net worth (
p-value = 0.000) after controlling the effect of age, gender, marital status, and education.
4.4. Economic Vulnerability and Participation in Development Programs
The effect of economic vulnerability on participation in development programs was measured by adopting a multiple regression analysis, which examined the effect of economic vulnerability on the number of years; number of training programs attended, number of hours of training programs, and total amount of economic loan received; controlling the effect of respondent’s age; gender; marital status, and education.
As presented in
Table 5, the VIF values of below 5 indicated a non-issue of multicollinearity. The
p-value for the ANOVA F test was 0.000, which denoted that at least one variable can be used to model the number of participation years. However, the
p-value of the Kolmogorov–Smirnov test of normality of the residuals using the initial sample (N = 450) was 0.000, which was less than 0.05, thus, failing to meet the assumption of normality. The unstandardized residual stem-and-leaf plot presented the outliers based on the unstandardized residual values. This study removed the outliers and proceeded to re-analyze the model using 295 samples. Accordingly, the Kolmogorov–Smirnov test of normality (N = 295) yielded a
p-value of 0.20, which was higher than 0.05, therefore, it had satisfied the assumption of normality.
Table 5 presents the standardized beta and
p-value from the findings using the 295 sample. The
r2 value was 0.654, which indicated that 65.4% of the variation in a number of participation years could be explained by the level of economic vulnerability, age, gender, marital status, and education. The findings presented in
Table 5 showed that the number of participation years had a significant (
p-value less than 0.05) positive effect (N = 450 and N = 295) on the level of economic vulnerability among low-income households in Kelantan. This study concluded that economically vulnerable households had begun participating in the programs of various development initiatives long before the non-vulnerable households.
The below 5 VIF values indicated the non-issues of multicollinearity. Meanwhile, the
p-value of the ANOVA F test was 0.000, thus indicating that at least one variable can be used to model the number of training programs attended. However, the Kolmogorov–Smirnov test of normality of the residuals using the initial sample (N = 450) yielded a
p-value of 0.000, which was less than 0.05, thus, failing to meet the assumption of normality. The unstandardized residual stem-and-leaf plot presented the outliers based on the unstandardized residual values. The removal of the outliers and re-analysis of the model using 142 samples was conducted. Hence, the
p-value of the Kolmogorov–Smirnov test of normality (N = 142) yielded 0.057, which was higher than 0.05, therefore, satisfying the assumption of normality.
Table 5 presents the standardized beta and
p-values from the findings using the 142 sample. The findings show a significant negative effect (N = 450 and N = 142) on economic vulnerability levels on the ‘number of training programs’ attended by respondents in Kelantan. This study concluded that economically vulnerable households attended a smaller number of micro-enterprise development training programs offered by the development organizations in Kelantan.
As for the VIF values below 5, they indicated non-issues of multicollinearity. The
p-value from the ANOVA F test was 0.000, which indicated that at least one variable can be used to the model number of program training hours. However, the Kolmogorov–Smirnov test of normality of the residuals using the initial sample (N = 450) yielded the
p-value of 0.000, which at less than 0.05, denoting a failure in meeting the assumption of normality. The unstandardized residual stem-and-leaf plot presented the outliers based on the unstandardized residual values. This study removed the outliers and proceeded to re-analyze the model using the 86 sample. Following this, the
p-value for the Kolmogorov–Smirnov test normality (N = 86) yielded 0.20, which was higher than 0.05, therefore, satisfying the assumption of normality.
Table 5 presents the standardized beta and
p-value from the findings using the 86 sample. As for the number of program training hours, the findings showed a significant (
p-value less than 0.05) negative effect (N = 450 and N = 86) on the economic vulnerability levels among the low-income Kelantanese households. Therefore, this study concluded that the economically vulnerable households attended a smaller number of training hours on programs offered by the development organizations in Kelantan.
Finally, the VIF values below 5 indicate the absence of multicollinearity issues. The
p-value from the ANOVA F test was 0.000 which indicated that at least one variable can be used to model the total amount of economic loan received. However, the
p-value of the Kolmogorov–Smirnov test of normality of the residuals using the initial sample (N = 450) yielded a
p-value of 0.000, which was less than 0.05, thus, failing to meet the assumption of normality. The unstandardized residual stem-and-leaf plot presented the outliers based on the unstandardized residual values. Following this, the study removed the outliers and re-analyzed the model using the 283 sample. Therefore, the
p-value for the Kolmogorov–Smirnov test normality (N = 283) yielded 0.095, which was higher than 0.05, therefore, satisfying the assumption of normality.
Table 5 presents the standardized beta and
p-value from the findings using the sample consisting of 283 respondents. The findings show a significant (
p-value less than 0.05) negative effect (N = 450 and N = 283) of economic vulnerability on the total amount of economic loan received by the low-income Kelantanese households. This study concluded that households’ level of economic vulnerability may prevent participants from further credit uptake.
4.5. Economic Vulnerability and Household Income
As for the average monthly household income, the
r2 value was 0.413, which means that 41.3% of the variation in the average monthly household income can be explained by the levels of economic vulnerability, age, gender, marital status, and education. The VIF values were below 5, thus indicating the absence of multicollinearity issues. The
p-value from the ANOVA F test was 0.000, which indicated that at least one variable can be used to model the average monthly household income. However, the Kolmogorov–Smirnov test of normality of the residuals using the initial sample (N = 450) yielded a
p-value of 0.000, which was less than 0.05, thus, failing to meet the assumption of normality. The unstandardized residual stem-and-leaf plot presented the outliers based on the unstandardized residual values. Therefore, this study removed the outliers and re-analyzed the model using the 308 sample. The
p-value for the Kolmogorov–Smirnov test normality (N = 308) was 0.20, which was higher than 0.05, therefore, satisfying the assumption of normality.
Table 6 presents the standardized beta and
p-value from the findings using the sample containing 308 respondents.
From the 308 samples, the r2 value was 0.757, which means that a 75.7% variation in the ‘average monthly household income’ can be explained by the levels of economic vulnerability, age, gender, marital status, and education. The VIF values for all variables are below 5, hence, denoting that there were no problems of multicollinearity. Subsequently, the p-value from the ANOVA F test was 0.000, which means that at least one variable can be used to model the ‘average monthly household income’ among the Kelantanese participants. As for the average monthly household income, the findings of the said both models (N = 450 and N = 308) received a negative effect on the respondents’ economic vulnerability level. Therefore, the findings of this study confirmed the negative effect of economic vulnerability on low-income Kelantanese households’ income. As for the effect of the control variables, the findings revealed that respondent age, gender, and education had a positive and statistically significant effect on the average monthly household income among the said Kelantanese participants.
4.6. Economic Vulnerability and Micro-Enterprise Income
The
r2 value of the average monthly micro-enterprise income was 0.380, which means that 38% of the variation in average monthly micro-enterprise income can be explained by the levels of economic vulnerability, age, gender, marital status, and education. The VIF values were below 5, thus indicating the non-existence of multicollinearity issues. Additionally, the
p-value from the ANOVA F test was 0.000, which means that at least one variable can be used to model the average monthly micro-enterprise income. However, the Kolmogorov–Smirnov test of normality of the residuals using the initial sample (N = 450) produced a
p-value of 0.000, which was less than 0.05, and accordingly failed to meet the assumption of normality. The unstandardized residual stem-and-leaf plot presented the outliers based on the unstandardized residual values. Subsequently, this study removed the outliers and re-analyzed the model using the 300 sample. The Kolmogorov–Smirnov test of normality (N = 300) produced a
p-value of 0.20, which was higher than 0.05, therefore, satisfying the assumption of normality.
Table 6 presents the standardized beta and
p-value from the findings using the sample containing 300 respondents.
From the same sample of 300, the r2 value was 0.744, which means that 74.4% of the variation in the ‘average monthly micro-enterprise income’ can be explained by the levels of economic vulnerability, age, gender, marital status, and education. Meanwhile, the VIF values for all variables were below 5, indicating that there were no multicollinearity issues. Subsequently, the p-value for the ANOVA F test was 0.000, which means that at least one variable can be used to model the ‘average monthly micro-enterprise income’ among the Kelantanese participants. Finally, the regression coefficients and p-values in said both models (N = 450 and N = 300) confirmed that economic vulnerability had a negative and statistically significant effect on the average monthly micro-enterprise income among the low-income Kelantanese households. As for the effect of the control variables, the findings revealed that respondent age, gender, and education have a positive and statistically significant effect on the respondents’ average monthly micro-enterprise income.
4.7. Economic Vulnerability and Micro-Enterprise Assets
In examining the effect of economic vulnerability on micro-enterprise asset net worth, the VIF values were below 5 indicating that there were no multicollinearity issues. Subsequently, the
p-value for the ANOVA F test was 0.000, which means that at least one variable can be used to model the ‘micro-enterprise asset net worth’. However, the Shapiro–Wilk test of normality of the residuals using the initial sample (N = 450) produced a
p-value of 0.000, which at less than 0.05, signaling a failure in meeting the assumption of normality. The unstandardized residual stem-and-leaf plot presented the outliers based on the unstandardized residual values. Consequently, this study removed the outliers and re-analyzed the model using sample containing 65 respondents. Thus, the
p-value for the Shapiro-Wilk test of normality (N = 65) yielded 0.254, which was higher than 0.05, therefore, satisfying the assumption of normality.
Table 6 presents the standardized beta and
p-value from the findings using the said sample.
From the same sample of 65 respondents, the r2 value was 0.997, which means that 74.4% of the variation in ‘micro-enterprise asset net worth’ can be explained by the levels of economic vulnerability, age, gender, marital status, and education. Meanwhile, the VIF values for all variables were below 5, thus, indicating the non-existence of multicollinearity issues. Subsequently, the p-value from the ANOVA F test produced 0.000, which means that at least one variable can be used to model the ‘micro-enterprise asset net worth’ among the Kelantanese participants. Finally, the regression coefficients and p-values in the said both models (N = 450 and N = 65) confirmed that the economic vulnerability had a negative and statistically significant effect on micro-enterprise asset net worth among the low-income Kelantanese households. As for the effect of control variables, the findings revealed that the respondent gender and education had positive and statistically significant effects on the respondents’ average monthly micro-enterprise income.