COVID-19 impact: Customised economic stimulus package recommender system using machine learning techniques

Introduction

The novel coronavirus 2019 (COVID-19) pandemic has created devastation in people’s lives worldwide, both socially and economically (Shah et al., 2020). As a result, governments have adopted various strategies aimed at reducing the pandemic’s impact, particularly the financial strain. The Malaysian government has introduced a series of economic stimulus packages to support various segments of its citizens. One such support is the Prihatin Rakyat Economic Stimulus Package (ESP) to cushion the impact of COVID-19 on low-income households after the first movement control in the country. The ESP consists of cash assistance, utility discount, moratorium, Employee Provident Fund and Private Remuneration Scheme (EPF and PRS) cash withdrawals and Credit Guarantee Scheme and Wage subsidies (Flanders et al., 2020). Following the implementation of ESP, the Department of Statistics Malaysia (DOSM) carried out a special survey from April 10 to April 24,2020 to better understand the implications of COVID-19 on the economy and households. The study included questions on social and economic factors and subsidy preferences.

A typical low-income household often bears considerable debt and has limited savings. When movement control was implemented, households that lost their income sources faced difficulties in accessing necessities, such as food and housing (Flanders et al., 2020). Even though the government offered ESP to help residents cope financially, the demands and desires of citizens in the event of a pandemic are unknown. For example, several households are reluctant to withdraw from EPF and PRS due to its reduction on their savings for old age. A personalised ESP can be built to reduce residents’ financial burden in this crisis if we can foresee their requirements and preferences for various subsidies, such as cash allowance, utility discount, moratorium or EPF and PRS withdrawals. Using data analytics and machine learning approaches, this study attempted to analyse survey data and construct predictive models for customised economic stimulus packages. The following research questions were put forward.

1. How can households that favour moratorium subsidies be identified?

2. How to find out which households seek utility discount subsidies?

3. How can households who desire EPF and PRS withdrawal subsidies be identified?

This study contributes to the literature by using four machine learning techniques on socioeconomic survey data and predicting household subsidy preferences. A comparison of the feature selection methods, such as Gini index, Gain–Ratio and various partitioning ratios of the training and test data sets were carried out. The outcomes of this study can help the government deliver better and improved stimulus packages in the future based on individual preferences.

Methods

For planning and execution, this study used the Cross-Industry Standard Process-Data Mining (CRISP-DM), which is the industry-independent de-facto standard for implementing data mining initiatives (Schröer et al., 2021). This process has six phases, namely, business understanding, data understanding, data preparation, modelling, evaluation and deployment. Figure 1 depicts the activities carried out in each phase, as further explained below.

Figure 1. Research methods.

Modelling

This phase employed a variety of machine learning techniques in order to achieve the study's goal of developing classification models. Different approaches, such as information-based learning, similarity-based learning, error-based learning, and probability-based learning, can be used to create classifiers (Kelleher et al., 2015). This research used information based learning (a decision tree algorithm family) as the best model for explaining decision logic. To create prediction models, we used four machine learning techniques: Decision Tree, Random Forest, Gradient Boosted Tree and Naïve Bayes. These four machine learning techniques were chosen from a literature review (Mostafa et al., 2021; Sangavi et al., 2020) and used to determine the optimal model by tuning their parameters. Feature selection methods, such as Gini index, Gain–Ratio and various partitioning ratios of the training and test data sets were also compared (Trivedi, 2020). Figure 2 depicts parameter tuning carried out in the modelling phase.

Figure 2. Flowchart for modelling and evolution process.

Results

The outcomes of this study were organised as descriptive analytics, model optimisation and findings. Descriptive analytics helps to understand the characteristics of each respondent and the relationship between variables. Table 2 provides the descriptive information on the respondents.

Figure 3 shows the various types of subsidies offered in the ESP. Among the 41,386 respondents, 72.2% were eligible to receive subsidies, 21.9% were newly applied, 3.2% were not eligible and 2.7% had appealed. Figure 3 also shows the most beneficial forms of support. The most popular type of subsidy was cash allowance, followed by moratorium, utility discounts and EPF and PRS cash withdrawals. The least preferred type was the credit guarantee plan and wage subsidies.

Figure 3. Most beneficial forms of assistance received under the Prihatin Rakyat ESP.

Following the descriptive analytics, the four machine learning techniques were applied to develop prediction models for each moratorium, utility discount and EPF withdrawals subsidies. Decision Tree, Gradient Boosted Tree, Random Forest and Naïve Bayes are subjected to parameter tuning to determine the best model and parameter values.

Table 3 to Table 6 show how the optimal model was obtained from each machine learning technique. Partitioning ratio indicates the training and test data. Gain ratio, Gini index and information gain were used to measure the quality of each predictor in classifying the target variable. The results show that the Gradient Boosted Tree and Naïve Bayes techniques performed well when 60% of the data were used to train the machine learning models and the other 40% was used for testing. Random Forest and Decision Tree techniques generated the best models when the training data were 80% and the test data were 20%. F-Score was used as the evaluation measure to select the optimal models. After identifying the optimal models, the best was selected among the four machine learning techniques. Table 7 shows the results. Gradient Boosted Tree outperformed the rest of the techniques in predicting the moratorium preference with 93.8% sensitivity, 82.1% precision and 87.6% F-score. When the data is partitioned with k= 5, and K=10, the findings reveal little difference in classifier performance, and the Gradient boosting tree still performs the best. When the data set is small, k-fold cross validation produces a superior model; but, when the data set is large, it produces no change. A current study backs up this conclusion (Marcot & Hanea, 2021)

A similar process was carried out to develop machine learning models for utility discounts and EPF and PRS subsidies. The results show that for both subsidies, Gradient Boosted Tree was identified as the best machine learning technique. Table 8 and Table 9 show that this technique can predict utility discount with 86% sensitivity, 82.1% precision and 84% F-score, as well as EPF and PRS with 83.6% sensitivity, 81.2% precision and 82.4% F-score, respectively.

Discussion

1. How can households that favour moratorium subsidies be identified?

To answer this research question, four classification models have been built using decision tree, gradient boosted tree, random forest and naïve bayes machine learning techniques. The optimal model from each of these techniques are determined by tuning their parameters and the details of parameter values are explained in the previous section. Finally, the best model from each of the four machine learning algorithms is compared, and the best model is chosen using the F-score performance evaluation measure. When the data division ratio is 60 percent training data and 40 percent testing data, Gradient Boosted Tree was shown to be the best machine learning model for predicting moratorium subsidies preferred households, with F-score =0.876 and sensitivity = 0.938. Hence this model is recommended for the deployment phase.

Although the gradient boosting tree can more accurately identify households that favour moratorium subsidies, it is difficult to interpret. It's because the relationship between each predictor and the target is modelled using a curve, making it difficult to explain how each predictor relates to the target. Machine learning techniques are always a trade-off between prediction accuracy and interpretability. In general, a method's interpretability reduces as its accuracy improves. (Hastie et al., 2021). Therefore, the decision tree model is utilised to create the ruleset in order to determine the general profile of families who favour a moratorium subsidy. Rule support refers to the number of respondents to whom this condition applies. Rule confidence indicates the probability of having a moratorium as the preferred subsidy. Table 10 shows the basic characteristics of families that choose moratorium subsidies with a rule support of 400 and above.

The first rule shows that households who prefer to have a cash allowance and their race is either Malay or Native Sabah/Sarawak or others, while those aged between 25 to 64 prefer moratorium. Table 11 explains the first rule indicating the general profile of households who prefer moratorium subsidies.

2. How to find out which households seek utility discount subsidies?

The four machine learning techniques described in the moratorium subsidies were applied to develop the classification model in order to identify the households who want utility discount subsidies. All of the procedures outlined in the preceding section were followed in order to find the optimal machine learning model. The gradient boosting tree outperforms the other three techniques, with a data partitioning ratio of 80:20, an F-score of 0.84, and a sensitivity of 0.86. Although the gradient boosting tree can more accurately identify households who seek utilities discount subsidies, it is difficult to interpret this model. As a result, decision tree rules were developed in order to comprehend the overall profile of households who seek utility discount subsidies. One such rule is presented in Table 12.

3. How can households who desire EPF and PRS withdrawal subsidies be identified?

To identify the households who want EPF and PRS withdrawal subsidies., similar to the moratorium and utilities discount subsidies classification models, decision tree, gradient boosted tree, random forest and naïve bayes techniques were used to develop the machine learning model. With a data partitioning ratio of 60:40 and F-scores of 0.824 and sensitivity of 0.836, the gradient boosting tree was found to be the best model compared to the others. Decision tree rules were developed to explain the general features of households who prefer EPF and PRS withdrawal subsidies, and one of the rules is displayed in Table 13.

The results imply that households that prefer moratorium subsidies did not favour other financial aids except cash assistance. By contrast, households that prefer for utility discounts, EPF and PRS withdrawals also chose moratorium subsidies and cash assistance. All households preferred cash assistance, which had the highest score among financial aids, followed by moratorium subsidies. Utility discounts, EPF and PRS withdrawals can be implemented according to the household income group preferences.

Wage subsidy and credit guarantee scheme were the least preferred financial assistance. First, the Prihatin wage subsidy is only for eligible Social Security Organisation (SOCSO) subscribers. Hawkers, small businesses and their employees might not subscribe to SOCSO and thus, ineligible to apply for financial aid. Second, the credit guarantee scheme was not preferred due to economic uncertainty from COVID-19. Economic uncertainty adversely affects household income, resulting in their inability to repay the loan instalments.

Conclusions

This study used data analytics and machine learning approaches to derive insights from the “Effects of COVID-19 on the Economy and Individual - Round 2” survey dataset. The CRISP-DM approach was applied to develop prediction models for households’ preferred subsidies, such as moratoriums, utility discounts and EPF and PRS using four machine learning algorithms, namely, Decision Tree, Random Forest, Naïve Bayes and Gradient Boosted Tree. For all three subsidies, the best predictive model was obtained by Gradient Boosted Tree. The findings can be used to design customised ESPs that effectively manage the economic burden of low-income households.

Data availability

Data used in this study were obtained from a survey dataset “Effects of COVID-19 on the Economy and Individual - Round 2,” available from the Department of Statistics, Malaysia (DOSM). A report published by the DOSM based on the survey can be viewed on the DOSM website. Access to this data requires application, as stated on the DOSM website. A guide for how to apply for dataset access is available on the Data Request page or requests for more information can be emailed to data@dosm.gov.my.