Economic Ripple Effect: The Effect of Public Transfer Payment Policy in Malaysia

Literature Review

Public transfer payment policy is a payment made by the government in terms of cash or in-kind subsidy to the people in need without expecting a quid pro quo. Public transfer payment can be made through cash transfer from the state to the people and it is considered as a non-exhaustive resource since it does not create an output directly (McConnell et al., 2012). There are two general types of transfer payment: in-kind and cash.

In Malaysia, both in-kind and cash transfer payment have their own significance to the Malaysian economy. Most of the in-kind transfer payments in Malaysia are concentrated in the state level where centralisation is non-existent as not all state governments provide the same in-kind transfer payment; for instance, Selangor Journal (2018) reported that the Selangor state government introduced several schemes to assist the poor households in terms of in-kind transfer payment such as Kasih Ibu Smart Selangor (KISS). KISS is an in-kind transfer payment provided by the state government of Selangor where the government offers a cashless groceries voucher for single mothers to purchase monthly groceries to feed their family. KISS scheme was first introduced in 2017, and the programme was exclusively implemented by the Selangor state government. On top of the non-centralised in-kind transfer payment of social security benefits like KISS, the irregularity of in-kind transfer payments such as unemployment compensation and civil service pension is making it a challenge to objectively quantify and collect data for in-kind transfer payments in Malaysia. The irregularity mentioned includes the low unemployment rate in Malaysia which holds little unemployment compensation and the varying average duration of a pension which is distributed to the pensioners, given the fatality rate of pensioners varies across sample sizes.

Compared to in-kind payment, cash transfer payment is the most common method being used by both the states and the federal government of Malaysia in standardisation. It was first crystallised in Malaysia back in the twentieth century (Saad & Abdullah, 2014). Specifically, in 2012, the federal government of Malaysia introduced another form of transfer payment for the low-income group called 1Malaysia People’s Aid (BR1M) (Nixon et al., 2017). BR1M is a form of transfer payment in terms of monetary assistance, which is provided to uplift the household income of the poor (Lagarde et al., 2009). The Ministry of Finance Malaysia (2017) reported that the federal government had allocated a total of MYR6.8 billion for the BR1M programme in the 2018 budget speech.

Due to the unavailability of data for multitudes of in-kind payments such as KISS, this research will focus more on cash transfer as a proxy to public transfer such as BR1M. Given that cash transfer payment is the most plausible proxy for public transfer payment in Malaysia, this article will consider only cash transfer payment when analysing public transfer payment policy in Malaysia.

There are two types of cash transfer: conditional cash transfer and unconditional cash transfer (Baird et al., 2013). According to Baird et al. (2013), in conditional cash transfer programme, the government will stipulate a condition before enabling the recipients to receive the cash transfer, meaning when a household receives conditional cash transfer from the government, they receive the money with due requirements for them to fulfil in order to receive it. On the contrary, in an unconditional cash transfer, the government provides cash to the recipient without any strings attached.

Likewise, Malaysian federal government adopted an unconditional cash transfer programme as its economic policy in 2012 to address the issue of the higher cost of living in the country and termed it as BR1M (Kamaruddina et al., 2013; Nixon et al., 2017). BR1M is an unconditional cash transfer programme where the recipients of BR1M have the freedom to spend on any goods and services which they deemed a priority. The significant difference between BR1M and other unconditional cash transfer programme around the world is that the lower-middle-income households are included as recipients, whereas most of unconditional cash transfer programmes in other parts of the world like Bolsa Familia programme in Brazil and Child Support Grant programme in South Africa are only meant for the poor and impoverished households (Aguero et al., 2006; Soares et al., 2010). Hence, BR1M’s coverage is more extensive in comparison to other unconditional cash transfer programmes.

Methodology

This research will mainly be using quantitative analysis through econographicology introduced by Ruiz Estrada (2007). Besides, this research will examine the emphasis on evaluation and analysis of public transfer payment policy through multidimensional perspectives.

Econographicology is the study of the economic graph using new types of figures and Cartesian spaces, and it is constructed based on a traditional three-dimensional space concept but represents 4-D, 5-D, 8-D, 9-D and infinity dimensions (Ruiz Estrada, 2007, 2011). Ruiz Estrada (2007, 2011) differentiated between traditional 2-D and 3-D Cartesian space with multi-dimensional (MD) Cartesian space, infinity (ID) Cartesian space and multi-functional (MF) Cartesian space. These three Cartesian spaces are a part of econographicology tools. Note that inter-linkage coordinate space is an extension model of MF Cartesian space which allows more variables and time period to fit into one graphical space.

The usage of ceteris paribus assumption or ‘all other things being equal’ in previous literatures has constrained us to analyse public transfer payment policy from a multidimensional lens. Ruiz Estrada et al. (2008) argued that ceteris paribus assumption is only applicable in a fixed time frame with selected variables but not for a dynamic time frame where time is moving continuously, parallelly with the changes of all relevant variables. With regard to analysing the real economic phenomenon happening in the real world, it is crucial to assume that time is dynamic.

This ‘time dynamic’ is a framework which indicates that time is moving continuously with every relevant variable changing simultaneously. It is the main factor why there is a need to analyse economic phenomena beyond ceteris paribus assumption in order to improve our understanding of the real impact of a given economic policy. In a ‘time dynamic’ framework, one should be able to examine all the variables that move simultaneously in an analysis which holds the premise that time is also moving dynamically. Hence, economics per se is complicated due to the complexity of the economic agents (Thaler, 2000), yet it should not stop us from examining economics principles using real-world data through ‘time dynamic’ framework.

Ruiz Estrada et al. (2008) believed in acknowledgement of complexity in economic phenomena where numerous variables are moving at the same time in a given ‘time dynamic’ framework which ceteris paribus is unable to capture. Hence, to complement the notable ceteris paribus assumption in economic analysis, Ruiz Estrada et al. (2008) suggested examining economics theories using Omnia Mobilis assumption. Omnia Mobilis assumption is defined as a phenomenon where ‘everything is moving’, and it uses the concept of ‘time dynamic’ when analysing a given economic phenomenon (Ruiz Estrada et al., 2008).

The significant difference between ceteris paribus assumption and Omnia Mobilis assumption is the applicability of Omnia Mobilis assumption in explaining the complexity of the real-world economic phenomena extensively with ‘time dynamic’ framework. With the existence of multidimensional graphs submitted by Ruiz Estrada (2007, 2011) called econographicology, it is now possible to visualise Omnia Mobilis assumption. Econographicology modelling includes the ‘time dynamic’ framework as part of its component when building the multidimensional graphs.

Ruiz Estrada (2011) introduced inter-linkage coordinate space which enables us to use Omnia Mobilis assumption and provide a multidimensional perspective for analysing public transfer payment policy. It is a suitable tool to model the interconnectivity between the public transfer payment policy in Malaysia and the overall Malaysian economy.

Ruiz Estrada (2011) used Figure 1 as the basis of the inter-linkage coordinate space where its general axes is [(A_0-j, A_1-j, A_2-j, A_3-j, A_4-j, A_5-j, …, A_n-j), (Y_i-0, Y_0-j, Y_1-j, Y_2-j, Y_3-j, Y_5-j, …, Y_n-j)] where n has a value from 0 to ∞. There are an infinite number of ratios. Each general axis will have an infinite amount of windows refraction (W_i-0, W_0-j, W_1-j, W_2-j, W_3-j, W_5-j, …, W_n-j) which will contain an infinite amount of sub-x and sub-y axes that will be represented by independent and dependent variables. Each windows refraction then will be combined under the same general axes with the application of the inter-linkage connectivity of windows refraction.

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Figure 1.

Source: Ruiz Estrada (2011).

Ruiz Estrada (2011) contended that inter-linkage coordinate space would be able to insert a large number of different functions and variables in different windows refraction simultaneously. With this functionality, inter-linkage coordinate space can be applied to include all relevant and critical variables when analysing any economic problem simultaneously without assuming any significant variable to remain constant.

For this study, general axes such as A_0-j, A_1-j, A_2-j, A_3-j, A_4-j, A_5-j, …, A _n-j in the inter-linkage coordinate space will be represented as periods. Since this research has a sample size of 10 periods, the general axes then can be expressed as A_1-j, A_2-j, A_3-j, A_4-j, A_5-j, A_6-j, A_7-j, A_8-j, A_9-j, A_10-j. Each general axis of A_1-j, A_2-j, A_3-j, A_4-j, A_5-j, A_6-j, A_7-j, A_8-j, A_9-j, A_10-j will have its multiple windows refraction such as W_i-0, W_0-j, W_1-j, W_2-j, W_3-j, W_5-j, …, W_n-j, which have function of a variable against one period of time.

There are a total of six variables involved in this research. These variables were chosen based on solid theoretical backgrounds. The source of the data is the Department of Statistics Malaysia, Ministry of Finance Malaysia and JAWHAR. JAWHAR is a government agency with a mission to improve the socio-economic development through governance and service delivery system in Malaysia. To construct the inter-linkage coordinate space, one needs to convert the data into the percentage change compared to the previous year. Hence, the selected variables with their units of measurement are provided as follows: (a) percentage change of the amount of public transfer payments distributed in Malaysia (), (b) percentage change of the level of B40 household income in Malaysia (Yp), (c) percentage change of the aggregate consumption in Malaysia (AgC), (d) percentage change of Malaysia gross domestic product (GDP) at current price (Y), (e) percentage change of enrolment rate in secondary school in Malaysia (Edu) and (f) percentage change of unemployed persons in Malaysia (U).

This research consists of 10 periods from 2008 to 2018, given the limited availability of data for the previous periods. There are 10 periods with 6 variables in each period and a total of 70 samples. Once all the data have been collected and converted, the data then need to be standardised with the overall mean to get a more accurate representation of the result when plotting them into the inter-linkage coordinate space.

As suggested by Jajuga and Walesiak (2000), data standardisation will provide a fair comparison between all variables in a single analysis. Since the base of our analysis is not the variability of the data, but instead the positioning of coordinate space in each window refraction, applying classical standardisation of z-score with the overall mean is the most suitable approach, as suggested by Jajuga and Walesiak (2000).

With classical standardisation of z-score, the standardised data are expected to have a mean of zero and a standard deviation of one. With a mean of zero, the data then can be plotted into inter-linkage coordinate space uniformly; for instance, for any standardised data that have a value of less than zero, it can be plotted into the coordinate space below the average line that has a yellow colour coding, as shown in Figure 2. If the standardised data have a value greater than zero, they can be plotted into the coordinate space above the average line. Since all the sub-x axes within general axes represent a period, the exact location of coordinate space should be set at the centre of sub-x axis for each window refraction to improve the scaling of the result. On top of that, the position of the coordinate space in each window refraction can be sorted according to the standardised value, given it is within the same general axes.

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Figure 2.

Source: The authors.

There are six different windows in each axis that can be expressed as W_1-j, W_2-j, W_3-j, W_4-j, W_5-j, W_6-j. Each window refraction is based on a combination of the sub-x axis (X_i-j) and its corresponding sub-y axis (Y_i-j), respectively. Each window refraction (W_1-j, W_2-j, W_3-j, W_4-j, W_5-j, W_6-j) will be followed by the coordinate space X_i-j, Y_i-j. The sub-x axis is represented by a period from 2008 to 2018 and we can express it as P_i-j, whereas the sub-y axis is represented by each variable mentioned above, which can be expressed as PTP _i-j ; Yp _i-j ; AgC _i-j ; Y_i-j; Edu _i-j ; U_i-j. Note that the term i represents the position of general axes, whereas the term j represents the position of the window refraction.

Once all windows refraction and general axis have been established, all windows refraction can be connected under the inter-linkage connectivity of windows refraction that has the same general axes, which can be represented as ®, as suggested by Ruiz Estrada (2011). In short, a single general axis with six window refractions can be drawn individually from two-dimensional perspectives with an expression of W_n-1 = (P_n-1, PTP_n-1) ® W_n-2 = (P_n-2, Yp_n-2) ® W_n-3 = (P_n-3, AgC_n-3) ® W_n-4 = (P_n-4, Y_n-4) ® W_n-5 = (P_n-5, Edu_n-5) ® W_n-6 = (P_n-6, U_n-6) where n denotes the number of periods in each general axis.

Since there are 10 periods and 6 window refractions, the overall expression can be written as follows:

General axis 1 (A₁ = period 1): W_1-1 = (P_1-1, PTP_1-1) ® W_1-2 = (P_1-2, Yp_1-2) ® W_1-3 = (P_1-3, AgC_1-3) ® W_1-4 = (P_1-4, Y_1-4) ® W_1-5 = (P_1-5, Edu_1-5) ® W_1-6 = (P_1-6, U_1-6);

Once an overall expression has been derived, we then continue to construct the initial state of the inter-linkage coordinate space before the data are plotted, as shown in Figure 2.

Based on Figure 2, inter-linkage coordinate space in this study consists of 10 general axes that represent the number of periods labelled P_i-j, and it increases in the clockwise direction. Between these 10 general axes, there is 1 central pillar that represents the percentage change of public transfer payment distributed in Malaysia labelled PTP _i-j . In each general axis, there are six different windows labelled W_i-j and five different sub-y axis labelled Yp _i-j , AgC _i-j , Y_i-j, Edu _i-j and U_i-j, respectively. Within each general axis, there is a middle line coloured in yellow representing the average amount for all axes.

Result Findings

In any economic policy implementation, there is a domino effect where consequences to the general economy do matter. Ruiz Estrada (2014) termed this as ‘economic wave’; when there are changes in a single variable, there will be implications to one or more variables which are all backed by literature reviews. Ruiz Estrada (2014) managed to visualise the economic wave using a multidimensional space diagram and proved that the economic wave does exist, using real data.

Based on the idea of the economic wave, the domino effect or the economic wave of the public transfer payment policy in Malaysia can be examined with the inter-linkage coordinate space. To comprehend the economic wave of public transfer payment policy, one needs to understand the chain effect that has a sound literature review on the given policy. Given the Omnia Mobilis assumption, when the Malaysian government implemented public transfer payment policy, the distribution of the cash transfer to all recipients was primarily constituted of B40 households. Therefore, a direct increase in the income of the B40 households is expected. As argued by Metwally (1983), a more impoverished household generally has a higher marginal propensity to consume, where it is expected that the recipients of the cash transfer will demonstrate increased consumption pattern due to the increase in their income. As ‘time dynamic’ is taken into account, the aggregate consumption of the economy is expected to increase due to the multiplier effect (Rawlings & Rubio, 2005; Suprayitno et al., 2013). An increase in aggregate consumption through public transfer payment can intensify the economic growth of the nation, as Yusoff’s (2011) study suggested. When the income of the recipient grows simultaneously along with the economy, the recipient of cash transfer has a higher tendency to provide better education to their children, which consequently would improve their level of education through higher participation in the early stages of education (Srinovita et al., 2016). Likewise, public transfer payment will affect labour’s participation rate in the workforce through higher productivity which in turn will reduce the number of unemployed people (Soares et al., 2010).

Based on Figure 2, we then can insert the standardised real-world data into the constructed inter-linkage coordinate space frame. By connecting the dots for all 10 periods with their respective coordinate space in the sub-y axis, a ripple-like motion will be formed on the complete inter-linkage coordinate space.

Economic Ripple Effect

Economic ‘Ripple Effect’ is a phenomenon that occurs in the complete inter-linkage coordinate space of public transfer payment policy that indicates a domino effect of the economic policy. It can be portrayed as follows:

A ‘Ripple Effect’ can be observed in Figure 3 where a ripple-like motion is noted, moving outside from the center, forming a circle around it. Note that the centre of the inter-linkage coordinate space denotes the distribution of public transfer payment policy. The ‘Ripple Effect’ occurs at the point where it starts with the purple curve and ripples down to the blue curve, to brown curve, to grey curve, to green curve and finally, to the pink curve. Imagine throwing a rock in a pond filled with water; when the rock touches the surface of the water, it will create a ripple-like motion. This ‘Ripple Effect’ can be used as an analogy where the implementation of public transfer payment policy is like the rock and, consequently, creates a domino effect that can be represented by its ripple-motion on the surface of the water. ‘Ripple Effect’ is a graphical proof with real-time data in econographicology which can be used to portray the domino effect in the economy through economic policy.

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Figure 3.

Source: The authors.

Economic Ripple Effect and Dynamic Economic Wave

To examine inter-linkage coordinate space at its full potential, it can be integrated with the idea of Ruiz Estrada’s (2014) economic wave into the same graphical form. All the economic waves can be drawn into Figure 3 and consequently be known as ‘Dynamic Economic Wave’, since the assumption of ‘time dynamic’ still holds where time and every variable is also moving simultaneously. Hence, the final state of inter-linkage coordinate space can be derived as follows:

Figure 4 contains the final graphical form of inter-linkage coordinate space that showcases the ‘Ripple Effect’ and ‘Dynamic Economic Wave’ at the same time. We can remove the inter-linkage coordinate space frame shown in Figure 4 to get a clear shape of the ripple-like motion of inter-linkage coordinate space.

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Figure 4.

Source: The authors.

Figure 5 shows the simplified version of the inter-linkage coordinate space without its frame. The purpose of Figure 5 is to allow researchers and policymakers to observe the overall impact of a given economic policy and in this case, public transfer payment policy. Figure 5 manages to showcase a clearer picture of the ‘Ripple Effect’ as well as the ‘Dynamic Economic Wave’.

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Figure 5.

Source: The authors.

Discussion and Conclusion

Inter-linkage coordinate space is an alternative tool that policymakers should use to examine every economic policy to improve the understanding of its impact on the overall economy. This graphical form introduced in econographicology may provide policymakers with a powerful visual tool to analyse the given policy problem and deliver a better explanation for the audience from a non-economic background. However, there are two specific limitations of inter-linkage coordinate space as part of econographicology tools in this study.

The first limitation of this study is the lack of data availability on public transfer payment in Malaysia. The current data used in this study were for the period 2008–2018, and they were collected separately from the Ministry of Finance and JAWHAR portal. The data prior to 2008 are unavailable. If the data for all six variables contain more than 10 periods, the result may be different and will provide more abundant information for analysis.

The second limitation for this study is the technique used in constructing the inter-linkage coordinate space. Given the limited data availability, inter-linkage coordinate space could not be constructed through computer graphical animation. Hence, the researcher drew every diagram related to inter-linkage coordinate space for this study manually by making use of all available tools like Microsoft Word that may have reduced the accuracy of data plotting. With the help of computer graphical animation, it is possible to draw inter-linkage coordinate space meticulously with the right scaling and data plotting to obtain a minutely accurate visual representation of the concomitant effect of this economic ‘Ripple Effect’ in the same graphical space.

Besides the limitation of inter-linkage coordinate space, there are areas for future research that one can explore from this study. Since this study has shown the ‘Ripple Effect’ and ‘Dynamic Economic Wave’ of public transfer payment policy graphically, future research should explore mathematical modelling of this research through Econophysics. An exploration of mathematical modelling of wave theory under fluid dynamics and quantum mechanics can be considered to integrate with mathematical economic modelling; for instance, an attempt to measure the wavelength of the economic wave should be made and this wavelength can represent the magnitude of the domino effect in a given economic policy. Theoretically, one can argue that the shorter the wavelength, the stronger is the domino effect of a given economic policy. Once the wavelength of the economic wave is quantifiable, future research can also expand to calculate the velocity of the wave, given the conditions that wave frequency remain constant; for example, Craik (2004) suggested that wave theory proposes the formula of wave velocity as the product of wave frequency and wavelength; it is plausible to quantify the wave velocity as well and this can be interpreted as the velocity of the domino effect in a given economic policy. Conceptually, the higher the velocity of the wave, the faster is the occurrence of the domino-effect in given economic phenomena which cover the lagged effect in the economy. Therefore, wave theory under fluid dynamic is an area that can be used to explore further the application of econographicology.