Public Debt Dynamics in Emerging Markets: The Case of Malaysia
Shariff Umar Shariff Abd. Kadir1,2*, Wong Hock Tsen2
1 Labuan Faculty of International Finance, Universiti Malaysia Sabah, Malaysia
2 Faculty of Business, Economics and Accountancy, Universiti Malaysia Sabah, Malaysia
*Corresponding Author: [email protected]
Accepted: 15 September 2021 | Published: 1 October 2021
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Abstract: This paper aims to investigate the impact of macroeconomic shocks on public debt dynamics in Malaysia. Time-series macroeconomic data spanning from 1980Q1-2020Q3 used in this study. The dynamic relationships are examined through impulse response functions (IRFs). The SVAR modelling approach is employed as it can capture the key characteristics of a small open economy. The results demonstrate that after a budget surplus shock, the government debt ratio falls, indicating a Ricardian regime. Domestic inflation triggered the government debt ratio to decline. Similarly, a reduction in the debt ratio as a result of a domestic output shock. The response of government debt to trade openness follows a similar pattern. In contrast, interest rate shock elevated government debt.
Keywords: Public debt, Fiscal policy, Malaysia
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1. Introduction
Despite the fact that theory explains the effectiveness of fiscal policy in stabilising economic fluctuations, active policy implementation has important consequences for fiscal position and government debt. . During the global economic crisis, for example, discretionary fiscal policy was actively implemented in the form of economic stimulus packages to stabilise the economy and control inflation (Kirsanova et al., 2009). However, such a measure has triggered the public debt to rise (Cherif & Hasanov, 2018).
Recently, the economic crisis triggered by the COVID-19 pandemic, which began in December 2019, has had a significant impact on economic activities. Global economic growth is expected to shrink to 5.2 percent by 2020, the worst slowdown in decades (World Bank, 2020).
Meanwhile, the government debt ratio in most countries is expected to rise. Government debt ratios are anticipated to grow by 20%, 10%, and 7% in developed, emerging, and low-income countries, respectively, in 2021. While developed countries have the ability to increase government spending through borrowing, emerging and low-income countries are finding it more difficult to increase debt due to the risk of a debt crisis (IMF, 2020).
As a new emerging market, Malaysia economy adversely affected by economic and financial crises such as the 2008 global financial crisis and the recent economic crisis triggered by the COVID-19 pandemic. Economic growth tumbled to a negative 17.1 percent in the second quarter of 2020, the lowest level since the Asian financial crisis, when GDP slipped 11.2 percent in the fourth quarter of 1998. The unemployment rate increased by 5.4 percent from
March to May 2020, a rise from 3.3 percent previously recorded. Inflation is expected to moderately decline from 1.0 percent in the fourth quarter of 2019 to 0.9 percent in the first quarter of 2020. This economic slowdown occurred as a result of the government's decision to impose movement control orders as well as standard operating procedures for all sectors to prevent the spread of the COVID-19 (BNM, 2020). In the meantime, the government debt-to- GDP ratio is expected to exceed the statutory limit of 55% of GDP. The government, however, has raised the debt ceiling to 60%. This measure is necessary for the government to increase spending to lessen the economic impact of the COVID-19 pandemic. Thus, the main challenges that policymakers face when dealing with high public debt are determining the appropriate timing, pace, and debt-reduction tools.
Based on the equation of intertemporal budget constraints, public finance reform through fiscal consolidation can help to reduce debt ratios. However, previous studies investigating the relationship between budget surplus/primary surplus and government debt found mixed results.
Few studies have found that fiscal policy consistently follows the Ricardian view, while others appear to align with the non-Ricardian point of view. The mixed results discovered in the literature warrant further investigation, particularly in emerging markets such as Malaysia.
Inflation shock is another determinant that can reduce debt ratio. Apart from that, high economic growth could lead to a decrease in the debt ratio (see Walsh, 2010). According to the
“efficiency hypothesis”, openness can improve government efficiency by increasing competition among countries. This argument predicts that as government efficiency improves, government spending and debt will decrease (see Dong, 2021). Hence, openness is another factor that can lead to a decrease in the government debt ratio.
In this paper, we provided empirical evidence in examining the public debt dynamics and focus on the effects of inflation, budget surplus, economic growth and trade openness shocks on government debt in Malaysia. This study uses time-series macroeconomic data and the relationship between public debt and macroeconomic variables are examined through impulse response functions (IRFs). The SVAR modelling approach is employed as it can capture the key characteristics of a small open economy (Cushman & Zha, 1997).
The main contributions of this study can be divided into two aspects. First, this study contributes to the implementation of government policy. In the context of government policy implementation, this study can provide information to policymakers in identifying the response of government debt to shocks of macroeconomic variables. High government debt affects the implementation of monetary policy. Thus, policymakers must construct effective strategies to reduce government debt. This study can contribute to the search for such strategies. In addition, this study can provide information to policymakers typically concerning threats to economic sustainability if fiscal policy sustainability is not satisfied.
Second, this study contributes to the literature by analyzing the public debt dynamic using the open-economy SVAR model. To be specific, this study considers the role of foreign variables in examining the relationship between government debt and macroeconomic variables. As Malaysia practices an open economy policy, the influence of foreign variables is relevant in modelling a small-open economy SVAR.
2. Literature Review
Previous studies investigating the response of debt ratio to fiscal policy shocks seem to be aligned with Ricardian and non-Ricardian regimes. A Ricardian regime is when fiscal policy adjusts its stance to satisfy government budget constraints (Sargent, 1982). Meanwhile, fiscal reacts in such a non-Ricardian regime if the policy does not respond to government budget constraints (Woodford, 1995). Past studies discovered that fiscal policy consistent follow the Ricardian regime among them were Bohn (1998), Canzoneri et al. (2001), Afonso (2002), Favero & Giavazzi (2007), Pehlivan & Balli (2016) and Caselli & Reynaud (2020).
Bohn (1998) among the first studies developed a model to test the fiscal policy reaction function. Bohn (1998) used the primary surplus to assess the fiscal policy reaction function and incorporated dynamic feedback from the level of government debt, government expenditure, and GDP gap. Bohn (1998) argues that the fiscal policy response to government debt is important in determining fiscal sustainability. The findings show that the increase in government debt induced fiscal policy to respond by increasing the budget surplus. This positive relationship demonstrates that fiscal authorities respond to government debt through implementing public finance reforms such as adjusting budget surpluses or addressing budget deficits.
Canzoneri et al. (2001) found a similar result using a recursive VAR model in examining fiscal policy response to government liabilities. Canzoneri et al. (2001) distinguish the Ricardian regime from the non-Ricardian regime by organising the primary surplus and liability.
However, the result holds supporting the Ricardian regime regardless of how the primary surplus and government liabilities are arranged on the VAR system. In another study, Afonso (2002) utilised panel data analysis to examine the fiscal policy reactions in 15 EU countries.
Afonso (2002) discovered an increase in government debt increased the primary surplus.
Favero & Giavazzi (2007) used government tax revenues to measure fiscal policy reaction towards government debt. Using SVAR model, Favero & Giavazzi (2007) proved that the positive shock of government tax revenue induces the government debt ratio to fall. However, this negative response only appears between 1980 and 2006 in the US. Focusing on the Common Wealth of Independent States, Pehlivan & Balli (2016) discovered that primary balance responds positively to the ratio of government debt to GD. This fiscal policy reaction is consistent with the Ricardian point of view. Despite this, positive relationships have been developed in some countries.
A recent study by Caselli & Reynaud (2020) examined the fiscal policy reaction function in 142 countries. In contrast to the previous study, the authors have incorporated fiscal rules into the primary surplus equation. The study findings revealed that as the government debt grew, the fiscal authority increased the primary balance. Caselli & Reynaud (2020) further states that fiscal rules encourage improved fiscal sustainability, which ensures the long-term sustainability of government debt.
Previous empirical studies support for non-Ricardian regime can be found in Abiad & Ostry (2005), Thams (2006), Attinasi & Metelli (2017) and Urquhart (2021). Abiad & Ostry (2005) investigates fiscal policy stance in 31 new emerging markets. Based on panel data analysis, Abiad & Ostry (2005) discovered that budget surpluses in new emerging markets do not respond to increases in government debt when the level of government debt exceeds 50 percent
of GDP. According to Abiad & Ostry (2005), this response proves that fiscal solvency conditions tend not to comply if government debt levels are recorded high.
Thams (2006) investigates the impact of fiscal policy in Germany and Spain. Using a Bayesian VAR model, Thams (2006) discovered that positive fiscal policy shocks increased government debt. This positive relationship implies that fiscal authorities do not respond to increases in government debt, thereby confirming the non-Ricardian regime. This regime, however, was only discovered in Germany, particularly after the country's reunification in 1991. Attinasi &
Metelli (2017) investigate the effects of fiscal policy shocks on government debt in 11 European Union countries. Attinasi & Metelli (2017) discovered evidence to support the non- Ricardian view that fiscal authorities do not respond to government debt. The result demonstrates that a positive shock to government tax revenue caused the level of government debt to rise rather than fall. Urquhart (2021) examines the link between public debt and macroeconomic variables in Paraguay. Using the SVAR model, the study discovered that a shock to primary surplus has no significant impact on public debt in the full sample, indicating a non-Ricardian regime.
3. Methods
Data and Variables Description
This study uses quarterly data from 1980:1 to 2020:3 to examine the relationship between public debt and macroeconomic variables (the data definitions and sources in Appendix 1). We select this period because Malaysia has gone through major transformations, such as the early 1990s shift in monetary policy strategy, and has encountered economic and financial crises, such as the 1997/1998 ASEAN financial crisis, the 2008 global financial crisis and the recent economic crisis triggered by the COVID-19 pandemic, which began in December 2019.
We incorporate foreign variables into our analysis of the effects of macroeconomic shock. The justification foreign variables considered due to the fact that Malaysia is a small open economy that is vulnerable to external shock. According to the globalisation index, the Malaysian economy, particularly the trade and finance category increased by nearly 24 percent in 2018 to 81 from 65 in 1980. This demonstrates that Malaysia global economic integration has been positive since 1980.
We divided the variables into two blocks; the first block consists of foreign variables, while the second block consists of domestic variables. The foreign block includes three variables:
world oil price, foreign national income, and foreign monetary policy. Oil price is based on WTI crude oil price. We include world oil price as Malaysia is a net oil exporter. Any changes in oil prices can affect the Malaysian economy. Foreign national income is represented by US output, specifically the US real GDP. Since the United States is a major trading partner of Malaysia, it was chosen to reflect foreign variables. On average, Malaysia total export to the United States accounted for 43% of total products from 1990 to 2019 (WITS, 2019).
Foreign monetary policy is the Federal Funds Rate (FFR). This variable is considered because any adjustment in the FFR will signal the state of the US economy and is expected to change the core indicators of US monetary policy, which will, in turn, affect the flow of macroeconomic fluctuations in small open economies (Karim & Karim, 2014).
The domestic block includes six variables: domestic national income, domestic inflation, government debt, trade openness, balance surplus and domestic interest rates. The real GDP is
used as the domestic national income. Domestic inflation reflects quarter-on-quarter percentage change of the consumer price index (CPI). Government debt is the ratio of government debt to GDP where it includes the sum of both internal and external debts.
The amount of goods and services exported and imported as a percentage of GDP is used to measure trade openness. The inclusion of trade openness is reasonable because it affects productivity factors, which can foster growth and national income (Mahmah & Kandil, 2018) but it also exposes countries, particularly commodity-producing countries, to external shocks that affect not only revenue but also expenditure (Combes & Saadi-Sedik, 2006). Furthermore, since Malaysia is a country with an open economy, this study takes trade openness into account.
The budget surplus expressed as a percentage of nominal GDP is defined as the sum of revenues minus total expenditure. The selection of budget balance as a fiscal stance is reasonable because it helps in assessing the impact of discretionary fiscal policy and automatic stabilisers. The interbank overnight rate is chosen as the domestic interest rate. As a central bank of Malaysia, Bank Negara Malaysia introduced a new interest rate framework in April 2004, declaring the Overnight Policy Rate to be the primary indicator of monetary policy. As a result, because OPR data became available only in 2004, the overnight interbank rate (IBOR) used to represent the stance of monetary policy. Among studies that used IBOR as a monetary policy stance are Ibrahim (2005) and Karim & Karim (2014). All variables are transformed by taking logarithms expect for interest rates taken in percentage, and the ratios such as government debt, trade openness and budget surplus measured as a percentage of GDP.
The effects of the 1997-98 Asian Financial Crisis (AFC), and the 2008 Global Financial Crisis (GFC) are captured by two dummy variables. AFC, for instance, has a value of one from 1997:4 to 1998:4 and a value of zero otherwise, and GFC has a value of one from 2008:1 to 2008:4 and a value of zero otherwise.
Econometric framework
Since Sims (1980) seminal paper, the VAR model has been widely used to explain the impact of macroeconomic shocks. Therefore, we apply a structural VAR (SVAR) model in investigating the impact of macroeconomic shocks on the public debt dynamic. In this study, we include both monetary and fiscal variables as Favero (2002) demonstrated that separately estimating the effects of fiscal and monetary policies would result in biased estimators.
The advantage of using SVAR model is that it captures the key features of a small open economy. Cushman & Zha (1997) stated that SVAR models are not only reliable but also provide valid results, particularly for small open economies. The relationship between public debt and macroeconomic variables can be explained using the SVAR model as follows:
AYt = V + C(L)Yt−p+ εt (1)
where A is a rectangular matrix that describes the contemporaneous structural relationship between variables, Yt is (n x 1) vector of variables included in a system, V is (n X 1) determinant vectors of variables (constants, trends and dummy variables), C(L) is (n X n) square matrix polynomial in the lag operator L, and εt is (n x 1) vector of structural error (in VAR terms, structured error is called innovation/surprise/impulse). The vector of structured error (εt) satisfies the conditions 𝐸(𝜀𝑡) = 0 and 𝐸(𝜀𝑡𝜀𝑡′) = 𝐼𝑛 is 𝑛 × 𝑛 of the identity matrix.
Equation (1) cannot be estimated using the OLS because there is a lag effect on the dependent variable. Thus, A matrix appears to be problematic because the structure error and regressor are correlated. This problem, however, can be solved by converting the equation (1) to its reduced form by multiplying 𝐴−1:
𝑌𝑡 = 𝐴−1𝑉 + 𝐴−1(𝐶1𝐿 + 𝐶1𝐿2+ ⋯ + 𝐶𝑘𝐿𝑘)𝑌𝑡+ 𝐴−1𝜀𝑡 or,
𝑌𝑡 = ∏0+ ∏1𝑌𝑡+ 𝜇𝑡 (2) where ∏0 = 𝐴−1𝑉, ∏1𝑌𝑡 = 𝐴−1(𝐶1𝐿 + 𝐶1𝐿2+ ⋯ + 𝐶𝑘𝐿𝑘)𝑌𝑡 and 𝜇𝑡 = 𝐴−1𝜀𝑡.
In equation (2), 𝜇𝑡 is a residual from reduced-form VAR that meets the conditions 𝐸(𝜇𝑡) = 0 and 𝐸(𝜇𝑡𝜇𝑡′) = ∑𝜇 is a positive and symmetric matrix that can be estimated from the data.
Given that the residual from reduced-form VAR (𝜇𝑡 ) and the structured error (𝜀𝑡) have the relationship 𝜇𝑡 = 𝐴−1𝜀𝑡 or 𝐴𝜇𝑡 = 𝜀𝑡, the variance-covariance matrix to capture this relationship is as follows:
𝐸(𝜇𝑡𝜇𝑡′) = 𝐴−1𝜀𝑡𝐴−1′𝜀𝑡′
= 𝐴−1𝐸(𝜀𝑡𝜀𝑡′)𝐴−1′ (3)
= 𝐴−1∑𝜀𝐴−1′
∑𝜇 = 𝐴−1𝐴−1′
The variance-covariance matrix (∑𝜇) has different elements with n(n + 1)/2. The number of these elements represents the maximum number of identifiable parameters in A matrix where 𝑛 represents the number of endogenous variables in the SVAR system. The variance-covariance matrix reflected the contemporaneous relations among the variables. However, A matrix contains 𝑛2 parameters, which exceeds the maximum number of parameters required by the SVAR system. As a result, the SVAR system faces identification problems.
The order condition introduced by Rothenberg (1971) can be used to solve the identification problems in the SVAR system. Lutkepohl & Kratzig (2004) stated that order condition is a standard criterion for resolving SVAR system identification problems. Based on this condition, the zero restrictions on the A matrix must be determined, and the sum of these restrictions is subject to the calculation (𝑛2− 𝑛)/2. After solve the identification problem, then the SVAR model can be estimated using the maximum likelihood estimation.
The short-run zero restrictions on the A matrix are imposed in this study, as shown in (4) in compact matrix form. The short-run restrictions in SVAR models can generate valid impulse responses as proposed by Christiano et al., (2006). In this study, the Cholesky decomposition is used to orthogonalize the variance-covariance matrix. Using this approach, the variables are ordered in a specific way, imposing some structure on the computation of the IRFs.
𝐴
[
1 0 0 0 0 0 0 0 0
𝛼21 1 0 0 0 0 0 0 0
𝛼31 𝛼32 1 0 0 0 0 0 0
𝛼41 𝛼42 𝛼43 1 0 0 0 0 0
𝛼51 𝛼52 𝛼53 𝛼54 1 0 0 0 0 𝛼61 𝛼62 𝛼63 𝛼64 𝛼65 1 0 0 0 𝛼71 𝛼72 𝛼73 𝛼74 𝛼75 𝛼76 1 0 0 𝛼81 𝛼82 𝛼83 𝛼84 𝛼85 𝛼86 𝛼87 1 0 𝛼91 𝛼92 𝛼93 𝛼94 𝛼95 𝛼96 𝛼97 𝛼98 1][
𝜇𝐿𝑂𝑃𝑡 𝜇𝐿𝑌𝑈𝑆,𝑡
𝜇𝐹𝐹𝑅𝑡 𝜇𝑇𝑂𝑡 𝜇𝐿𝑌𝑀𝑡
𝜇𝐵𝑆𝑡 𝜇𝐺𝐷𝑡 𝜇𝐼𝑁𝐹𝑡
𝜇𝑅𝑡 ] =
[ 𝜀𝐿𝑂𝑃,𝑡 𝜀𝐿𝑌𝑈𝑆𝑡
𝜀𝐹𝐹𝑅 𝑡 𝜀𝑇𝑂𝑡 𝜀𝐿𝑌𝑀𝑡
𝜀𝐵𝑆𝑡 𝜀𝐺𝐷𝑡 𝜀𝐼𝑁𝐹𝑡
𝜀𝑅𝑡 ]
(4)
Based on equation (4), the variables are orders as follows: LOP – world oil price, LYUS, - foreign national income, FFR – foreign monetary policy, TO – trade openness, LYM – domestic national income, BS – budget surplus, GD- government debt, INF – domestic inflation, and R – domestic monetary policy. We note that the results could be sensitive to variable orderings, hence theoretical considerations are used in this paper (e.g. Bernanke, 1986).
For instance, the foreign variables (LOP, LYUS, FFR) are placed ahead of the domestic variables (TO, BS, LYM, GD, INF, R) and are regarded as fully exogenous to the domestic variables. This indicates that the domestic variables respond to the foreign variables contemporaneously, but not otherwise. Prior literature on SVAR models that has guided the order in which the foreign block leads the domestic block are Cushman & Zha (1997), Brischetto & Voss (1999), Kim & Roubini (2000) and Zaidi et al. (2016).
Furthermore, the placement of trade openness above all domestic variables explains that this variable is assumed to have a contemporaneous effect on all domestic variables. This ordering is consistent with Jin (2006) and Ahiakpor et al., (2019). The domestic national income is then arranged above the fiscal variable. This assumption is reasonable because fiscal variable has a lag effect on domestic income (see Blanchard & Quah, 1989). In the empirical literature, it is common to place government spending before output and tax revenues after output. As we used budget deficit (which include both expenditure and tax revenues) as a fiscal policy variable, we leave open the possibility that economic activity could affect fiscal policy contemporaneously. Petrevski et al. (2015) employed the same ordering in their study.
The arrangement of government debt below domestic income and fiscal variable is consistent with Petrevski et al. (2019). The domestic monetary variable (interest rate) is arranged last, in accordance with the literature on the lag effects of monetary policy. In this study, all variables are treated as jointly determined; no a priori assumptions regarding the exogeneity of any of the variables in the system are made.
4. Results
Preliminary analysis
Table 1 provides the results of the augmented Dickey-Fuller (ADF) tests to check the stationary for each variable. The tests indicate that only five variables have reached stationary in the level form. Specifically, foreign and domestic monetary policy found to be stationary in level with constant and time trend, government debt stationary with constant, whereas budget surplus and domestic inflation both stationary with constant and constant with time trend. The other variables identified as nonstationary in the level form.
Table 1: ADF test result
Variables level First difference
constant Contant & trend constant Contant & trend
LOP -1.816(0) -2.606(0) -5.372(7)*** -5.340(7)***
LYUS -2.386(2) -0.579(2) -7.198(1)*** -7.619(1)***
FFR -1.699(11) -4.171(13)*** -4.395(9)*** -4.346(9)***
TO -0.379(8) -1.378(8) -3.589(13)*** -4.050(13)***
LYM -0.840(8) -2.021(6) -5.767(7)*** -5.792(7)***
BS -3.708(11)*** -3.293(11)* -4.439(10)*** -5.055(11)***
GD -2.676(11)* -2.442(12) -3.094(3) ** -1.989(11)
INF -8.954(0)*** -9.424(0)*** -7.180(8) *** -7.175(8)***
R -2.215(9) -3.801(9)** -4.459(8)*** -4.448(8)***
Note: (***), (**), and (*) indicates significance at the 1%, 5%, and 10% levels, respectively. For the constants, the τ (tau) -statistic values were -3.47, -2.87, and -2.57 for the 1%, 5%, and 10% significance levels, respectively. The τ (tau) -statistic values for constants with time trends were -4.01, -3.43, and - 3.14 for significant levels of 1%, 5%, and 10%, respectively. Figures in parentheses () represent the optimal lag as determined by the Akaike Info Criterion (AIC).
The presence of either deterministic or stochastic trends could induce nonstationary variables to present. This leads to the question of whether the SVAR model should be specified in terms of first differences or levels. Sims (1980) and Sims et al. (1990) stated that the objective of a VAR is to determine the interrelationships between variables, not to estimate the parameters.
Thus, they advise against differencing and suggest that variables in model VAR be in level even if the variables contain a unit root. Furthermore, even if the variables have unit roots, a VAR model at the level can be estimated, so potential cointegration restrictions are ignored.
This is frequently practised in SVAR modelling to avoid imposing too many restrictions (Lutkepohl & Kratzig, 2004). Therefore, the SVAR model is specified in levels following their recommendation.
In this study, the optimal lag we used in the VAR system is six, as suggested by the Akaike information criterion (AIC). The selection of 6 lag sufficient to eliminate second- and fourth- order autocorrelation in the VAR system as shown in Table 2. Meanwhile, estimates from the VAR companion matrix reported that the eigenvalues are less than one. If the eigenvalue is less than one, the VAR (p) process is said to be stable (see Lütkepohl, 2005).
Table 2: Lag length selection of VAR
Equation k AR(2) AR(4)
LOP,LYUS,FFR,TO,LYM,BS,GD,INF,R 1 101.962(0.06) 200.391(0.00) 2 121.163(0.00) 195.920(0.00) 3 138.524(0.00) 162.332(0.00) 4 121.629(0.00) 92.669(0.18) 5 113.011(0.01) 80.893(0.48)
6 72.175(0.72) 76.231(0.63)
Note: k is number of lag as shown in the second column: third and fourth columns show the value of LM-stat and 𝑝 − 𝑣𝑎𝑙𝑢𝑒𝑠 in parentheses ().
Impulse response function (IRF) Ricardian and non-Ricardian Regimes
Figure 1 summarizes the result of the impulse response for government debt to a positive shock in budget surplus. The solid line represents estimated responses, and the two dashed lines represent confidence intervals. This confidence interval constructed using Hall bootstrap method with a confidence level of 90 percent and number of bootstrap repetitions of 1000 from the original sample data. The effect is considered significant when this band excludes zero. The result indicates that the government debt responds negatively and significantly following
budget surplus shock. This demonstrates a one-unit standard deviation increase in budget surplus causes a decrease in government debt. The statistically significant responses occur between the first and fifth quarters after the shock. The result suggested evidence of the Ricardian regime. This indicates fiscal authority in Malaysia increase budget surplus whenever government debt rises to satisfy the budget constraint. The result consistent with previous empirical studies among them were Baharumshah et al. (2017), Khalid et al. (2018) and Cherif
& Hasanov (2018).
Figure 1: Response of government debt to budget surplus shock
Response of government debt to other macroeconomic variables
Figure 2 depicts the relationship between government debt and other macroeconomic variables.
Government debt is seen to react negatively and significantly to the positive shock of domestic inflation, as shown in (a). The statistically significant responses occur in the first quarter and reoccur between the third and the sixtheen quarters. This suggests that the shock of domestic inflation can reduce government debt ratio. This finding is consistent with the expectation of a government budget constraint equation in which unexpected inflation or inflationary shocks cause a reduction in government debt (Walsh, 2010). Previous empirical studies have shown a negative relationship between government debt and inflation, including those by Reinhart &
Rogoff (2010), Hall & Sargent (2011), Aizenman & Marion (2011) and Cherif & Hasanov (2018).
The response of government debt to the domestic national income shock is also consistent with the theory as shown in (b). The government debt ratio discovered to decline as domestic national income raised. The findings consistent with the government budget constraint equation stated that economic growth can reduce the government debt-to-GDP ratio. A recent study, for example, Cherif & Hasanov (2018) discovered that economic growth reduces government debt.
In addition, domestic monetary policy shock has a positive effect on government debt as shown in (c). The finding implies that a contractionary monetary policy induces the accumulation of government debt. The government debt response is consistent with economic theory, which states that rising financing costs lead to increased debt accumulation (Melecky & Melecky, 2011).
In figure (d), initially, the government debt response to trade openness is positively and significantly. However, the reaction shifted negative and significant in the third quarter and reoccur in the sixth quarter and continue. Government debt response supports both the
“compensation hypothesis” and the "efficiency hypothesis." According to the “compensation hypothesis”, international economic integration exposes countries to external shocks; to counteract the instability created by globalisation, governments may increase their roles or engage more actively. Increased government intervention necessitates increased compensatory public spending, which could be partially funded by domestic and/or international government borrowing.
The “efficiency hypothesis” stated that openness increases government efficiency by intensifying competition among countries. As a result of increasing efficiency, this argument predicts that government spending and debt will decrease (see Dong, 2021). Therefore, we can conclude that the positive response of government debt is in line with the “compensation hypothesis”, although it occurs initially. The negative reaction of government debt is consistent with the “efficient hypothesis”, which is more valid as government debt decreases from the sixth quarter and continue.
(a) Response of GD to INF (b) Response of GD to LYM
(c) Response of GD to R (d) Response of GD to TO
Figure 2: Response of government debt to other macroeconomic variables (a) Inflation (b) domestic income (c) domestic monetary policy (d) trade openness.
5. Discussion and Conclusion
The analysis of public debt dynamics is important for fiscal authorities in formulating strategies for reducing government debt. Although the literature found mixed results regarding fiscal policy behaviour (Ricardian vs. non-Ricardian), a detailed empirical study, specifically for Malaysia, is necessary. This is due to the fact that the active implementation of fiscal policy in Malaysia, which causes government debt and government spending to continue rising, can have implications for the implementation of monetary policy in fulfilling the mandate of price stability. Hence, this study analyses public debt dynamics and focus on the effects of fiscal policy, inflation, interest rate, economic growth and trade openness shocks on reducing public debt.
In this study, an open-economy structural VAR (SVAR) used to analyse public debt dynamics.
There are several conclusions can be drawn from the empirical finding. First, the fiscal policy response discovered to be consistent with Ricardian regime. The positive shock of the budget surplus led to a reduction in the government debt ratio. Second, government debt has been observed to be decreasing as a result of domestic inflation shock. This finding demonstrates that rising inflation can lead to a reduction in real government debt. Third, positive economic growth can help to reduce the government debt ratio. Fourth, trade openness has a favourable effect on government debt reduction, as predicted by the "efficient hypothesis".
These findings have several implications for the implementation of fiscal and monetary policy.
First, since the response of government debt to budget surplus is negative, indicating that the fiscal authority in Malaysia satisfying intertemporal budget constraints. Thus, the implementation of public finance reforms through fiscal consolidation can ensure the sustainability of debt. Second, the trade openness policy helps to reduce government debt.
However, the government must take precautions because openness increases the vulnerability of the Malaysian economy to external shocks. Third, since inflation can reduce real government debt, monetary authorities must exercise greater caution when implementing a contractionary monetary policy to combat inflation, as this measure can increase the cost of government debt.
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Appendix 1
Table A1: Definitions and Sources of Variables
Variables Definition Unit Source
Foreign Variables
𝑶𝑷 WTI (West Taxes Intermediate) US$ per barrel, seasonally
adjusted
Federal Reserve Economic Data (FRED)
𝒀𝑼𝑺 US real GDP Billion US$, base: 2012,
seasonally adjusted
Federal Reserve Economic Data (FRED)
𝑭𝑭𝑹 Federal Funds Rate Percentage (%), seasonally adjusted Federal Reserve Economic Data (FRED) Domestic Variables
𝒀𝑴 Real GDP MYR million, base: 2010,
seasonally adjusted
Central Bank of Malaysia
𝑹 Overnight Interbank Rate (IBOR) Percentage (%), seasonally adjusted International Financial Statistics (IFS) 𝑰𝑵𝑭 Inflation Rate Percentage (%),seasonally adjusted International Financial Statistics (IFS) 𝑮𝑫 Government Debt Ratio (Sum of Internal Debt
and External Debt)
Percentage (%) of Nominal GDP, seasonally adjusted
Ministry of Finance Malaysia (MOF)
𝑻𝑶 Trade Openness (Sum of Total Export and Total Import)
Percentage (%) of Nominal GDP, seasonally adjusted
Department of Statistics Malaysia (DOSM)
𝑩𝑺 Budget Surplus (Total Government Revenue Minus Total Government Expenditure)
Percentage (%) of Nominal GDP, seasonally adjusted
Central Bank of Malaysia
