Vol. 15 No.1, Februari 2022

ISSN : 2301-8968

EKONOMI

KUANTITATIF

TERAPAN

JEKT

The Effect of Banks and Cooperatives in Improving Welfare Inayati Nuraini Dwiputri, Lustina Fajar Prastiwi, Grisvia Agustin

ISSN 2301-8968

Denpasar

Februari 2022

Halaman

1-161

The Role of Social Capital with Local Wisdom in Household Food Security in Bali Province Putu Ayu Pramitha Purwanti, Ida Ayu Nyoman Saskara

Middle Income Trap In A Macroeconomic Perspective A Case Study In Indoensia Apip Supriadi

Trade-Environment Triangle in Indonesia: Ecological Footprint Approach Kuratul Aini, Djoni Hartono

Social And Financial Efficiency Of Lembaga Perkreditan Desa Kajeng Baskara

The The Relationship Between Fiscal Policy And Civil Liberty On Per Capita GDP In Indonesia During 1980-2018

Vita Kartika Sari, Malik Cahyadin

The Effect Of Fiscal Decentralization On Economic Growth: A Study Of The Province Level In Indonesia Setyo Tri Wahyudi, Lutfi Kurniawati

The United States’ Monetary Policy Spillover Effect Against Rupiah -Us Dollar Exchange During Usa – China Trade War

Andryan Setyadharma, Anisa Rahmawati, Anisa Rahmawati

Affecting FactorsTrans Land Function In Bali

I Wayan Sudemen, I Ketut Darma

The General Allocation Fund (DAU) Formulation Policy: Incentives or Disincentives to the Fiscal Independence of Local Governments Kun Haribowo, Latri Wihastuti

Impact Of Rural Development Program On Agriculture Production and Rural-Urban Migration In Indonesia Murjana Yasa, Wayan Sukadana, Luh Gede Meydianawathi

Volume 15    Nomor 1

JEKT ♦ 15 [1] : 21-35

eISSN : 2303 – 0186


Keterkaitan antara Kebijakan Fiskal dan Kebebasan Sipil terhadap GDP per Kapita di Indonesia selama tahun 1980-2018

ABSTRAK

Kebijakan fiskal mempunyai kontribusi terhadap tingkat kesejahteraan masyarakat. Banyak temuan empiris menjelaskan keterkaitan antara komponen fiskal dengan per capita income. Selain itu, kebebasan sipil juga menjadi faktor pendorong kehidupan ekonomi masyarakat semakin maju. Dengan demikian, studi ini akan mengestimasi keterkaitan antara kebijakan fiskal dan kebebasan sipil terhadap per capita GDP di Indonesia selama 1980-2018. Model estimasi VAR dan Bayesian VAR dipilih karena relevan dengan estimasi variabel ekonomi yang bersifat a-theoretic. Temuan empiris menunjukkan bahwa tax ratio berkontribusi signifikan menghambat peningkatan per capita GDP, indikator hambatan berkelompok berakibat merusak per capita GDP, tetapi access to justice dapat memberi kesan perbaikan per capita GDP. Dengan demikian, pemerintah Indonesia sepatutnya dapat mendorong alokasi fiskal untuk program strategis dan produktif. Selain itu, pemerintah Indonesia juga seharusnya melindungi dan menjamin kebebasan sipil sesuai peraturan perundang-undangan yang berlaku.

Kata Kunci: pengeluaran pemerintah, ratio pajak, hutang luar negeri, indikator kebebasan sipil, GDP per kapita

JEL Classification: E01, E02, E62, H63

The Relationship between Fiscal Policy and Civil Liberty on Per capita GDP

in Indonesia during 1980-2018

ABSTRACT

Fiscal policy has a contribution to community welfare. Many empirical findings explained the relationship between the fiscal component and per capita income. In addition, civil liberty is a driving factor in economic life. Thus, this study estimated the relationship between fiscal policy and civil liberty on Per capita GDP in Indonesia during 1980-2018. The VAR and Bayesian VAR estimation models were selected and relevant to the a-theoretic economic variables. The empirical findings showed that the tax ratio contributed significantly to inhibit the increase in Per capita GDP, barriers to parties resulted in damaging Per capita GDP, but access to justice improved Per capita GDP. Thus, the Indonesian government should be able to encourage fiscal allocations for strategic and productive programs. In addition, the Indonesian government should also protect and guarantee civil liberties in accordance with applicable laws and regulations.

Key words: government expenditure, tax ratio, external debt, civil liberty, Per capita GDP

JEL Classification: E01, E02, E62, H63

INTRODUCTION

Fiscal policy is a driver of economic performance and is influenced by economic and non-economic factors (Baldacci, Hillman, and Kojo, 2004; Canh, 2018; Martinez-Vazquez, et al, 2007). In Indonesia, the role of fiscal policy has become significant after the economic crisis, especially tax and government expenditure (Arin, Braunfels and Doppelhofer, 2019). In theory, the Solow Model (1956) explained that tax and government expenditure have no effect in the long run, but have a transitional impact on output.

Barro (1990) stated that fiscal policy as an endogenous variable can have temporary or permanent effects. Furthermore, the Keynesian theory explains the important role of government in encouraging aggregate demand to achieve full employment. If the economy is below full employment, aggregate demand can be increased by increasing government expenditure or lowering the tax ratio.

This study focused on the relationship between indicators of fiscal policy and Per capita GDP. Several indicators of fiscal policy are government expenditure, tax ratio, and external debt. Thus, the problem formulation is "Did fiscal policy contribute significantly to Per capita GDP in Indonesia during 1980-2018?". Surprisingly, Maipita, Jantan, and Razak (2010) indicated that subsidies and income transfers had a positive impact on Indonesian living standard, even though they had a negative impact on other household sectors and reduced aggregate income.

Arin, Braunfels, and Doppelhofer (2019) found that tax is still the main source of income in the medium and long term in OECD countries. Furthermore, an increase in institutional quality has a positive

correlation with the effectiveness of fiscal policy in emerging countries (Canh, 2018).

At the macroeconomic level, economic activity is also influenced by institutional factors such as political stability and democracy. Knutsen (2012) states that democracy is positively correlated with economic growth. Currently, Indonesia is trying to improve its institutional conditions as described by Rock (2017) that the Indonesian government is strengthening state institutions, implementing decentralization, revising political regulations, and reducing separatist movements.

The influence of democracy on economic growth continues to be studied by researchers because democracy can encourage more efficient resource allocation (Tavares and Wacziarg, 2001; Drury, Krieckhaus, and Lusztig, 2006; Narayan and Smith, 2006; Doucouliagos and Ulubaşoğlu, 2008; Younis, et al, 2008; Knutsen, 2012; Amir-ud-Din and Khan, 2017; Bozkurt, Altiner, and Toktaş, 2018).

Furthermore, civil liberty can be reflected in several indicators such as freedom and active participation in democracy, academic freedom, and the achievement of basic needs. Based on the findings, the democratic activity can have positive implications for economic development in developed countries and negative implications in developing countries (Piatek, Szarzec, and Pilc (2013). Democracy can involve many aspects such as property rights, business rules, labor, and market mechanisms which can boost economic productivity (Doucouliagos and Ulubaşoğlu, 2008).

This study estimated the implications of civil liberties indicators on Per capita GDP in Indonesia. According to the World Bank,

civil liberties indicators are access to justice (AJ), basic welfare (BW), civil liberties (CL), freedom of academic and cultural expression (FACE), gender equality (GE), and human right protection (HRP). The detailed descriptions of these indicators can be found in Appendix A.

This study contributed to the existence of the literature in several forms. First, income and expenditure indicators in fiscal policy were used to estimate their effect on Per capita GDP in Indonesia. Second, 11 indicators of civil liberties were selected to measure the implications for Per capita GDP. Third, VAR and Bayesian VAR were applied to estimate the empirical model. Fourth, the policy has implications for optimizing the allocation of external debt for strategic and productive programs, ensuring civil liberties based on statutory regulations, and integrating or synchronizing central and regional government policies with the support of efficient and productive bureaucratic qualities.

After the New Order era, around 1999, democratic activities of Indonesian society were directed at a better level and quality (Rock, 2017; Rosser and Wilson, 2012) so those negative excesses could be reduced (Drury, Krieckhaus, and Lusztig, 2006). These negative excesses include corruption, restrictions on participation and civil liberties, the absence of economic justice, and the absence of legal certainty. The aim of implementing democracy is justice in the economic aspect. In fact, the existence of parties with higher material potential tends to be more influential in politics (Winters, 2013). Thus, the democratic system adopted by many countries in the world is a system of political freedom that encourages economic performance (Piatek, Szarzec and Pilc, 2013).

METHOD

Per capita GDP cannot be achieved if the fiscal policy does not specifically and strongly encourage economic activity and social welfare. In addition, the government must guarantee the freedom to provide access to creativity and innovation so that people can increase their income. Thus, this study estimated the relationship between fiscal policy and civil liberties on Per capita GDP in Indonesia during 1980-2018. Several secondary data were used in the Vector Autoregression (VAR) technique and Granger Causality Test (GCT) estimations.

This study used Per capita GDP (GDPPC), fiscal indicators, and civil liberty indicators data. Fiscal indicators consist of government expenditure (GFCE), tax ratio (TR), and external debt stock (EDS). Meanwhile, civil liberty indicators consist of access to justice (AJ), barriers to parties (BP), basic welfare (BW), civil liberties (CL), civil society participation (CCP), election free and fair (EFF), freedom. of academic and cultural expression (FACE), freedom of association and assembly (FAA), freedom of movement and residence (FMR), gender equality (GE), and human rights protection (HRP). These data were published by the World Bank. The explanation of research variables can be seen in Appendix A.

In theory, Arin, Braunfels, and Doppelhofer (2019) identified two theories describing the relationship between fiscal policy and output (growth). First, the neoclassical growth model explains that in the short run, there were implications for the transition of tax and expenditure on output but in the long run, it was not significant. Second, the endogenous growth model stated that fiscal policy has a significant impact on both transitional and permanent growth.

This study used a causal/a-theoretic approach to estimate the relationship between fiscal policy and civil liberties on Per capita GDP in Indonesia. This study used the VAR estimation (Gujarati, 2003).

The empirical model can be explained by the following equation:

GDPPCt = a0+ β1GDPPCt-1 +

β2∑‰FPt + β3∑‰CLt + εt(1)

FPt = a0+β1 ∑‰ι FPt-ι + β2GDPPCt + β3∑‰CLt + εt(2)

CLt = a0+β1 ∑‰ CLt-ι + β2GDPPCt + β3∑‰FPt + εt(3)

GDPPC stands for Per capita GDP. FP stands for indicators of fiscal policy while CL stands for indicators of civil liberties. Thea0 is the intercept, while the β is parameter of independent variables, ε is error term, and t is the 1980-2018 period.

The estimation of 1-3 equations used robustness checking with Bayesian VAR (BVAR) as stated by Miranda-Agrippino and Ricco (2018). BVAR is a relevant a-theoretic estimation model in economic analysis.

The causal estimation used was the Granger causality test (GCT). GCT was used to deepen the VAR estimation results on (1) - (3) equations. The basic model of GCT estimation has been explained by Gujarati (2003). Thus, Granger empirical model is as follows:

GDPPCt = a0+β1 ∑‰ FPt + β2 ∑‰ CLt + εt(1)

FPt = ao+ β1GDPPCt +   β2 ∑‰ CLt +

εt(2)

CLt = a0+ β1GDPPCt +

β2∑‰FPt + εt(3)

The difference in the empirical equation between VAR, BVAR, and GCT lies in the lagged of the dependent variable as the independent variable. GCT does not make the lagged of the dependent variable as the independent variable. Meanwhile, the third equation is an a-theoretic approach.

RESULT AND DISCUSSION

Many scholars have found the relationship between institutional indicators and fiscal policy indicators on economic growth. This study focused on indicators of fiscal policy and indicators of civil liberties. Plümper and Martin (2003) described that countries with high levels of democracy can encourage an increase in per capita income.

This study estimated the relationship between fiscal policy and civil liberty on Per capita GDP in Indonesia during 1980-2018. This study conducted unit root tests (URT) on time series data. The URT used Augmented Dickey-Fuller (ADF) and Phillip-Perron (PP) approaches on the level and 1st difference. The results of the URT can be seen in Table 1. ADF test described that at the level, several variables had stationary, namely HRP (intercept, intercept, and trend), TR, and BW (intercept and trend). Meanwhile, all the variables had stationary at 1st difference. Thus, these variables can be cointegrated in I (1). The results of this unit root test were relevant to the results of the PP test.

Then, the lag length determination was conducted. There are several methods to determine the lag length, such as the Akaike information criterion (AIC) with a value of 10.288 which indicated that lag 1 was the correct lag. In addition, the final prediction error (FPE) and Hannan-Quinn information criterion (HQ) values were 1838.216 and 10.533 respectively which indicated the lag 1.

Table 1. Results of Unit Root Test

Variables

ADF

PP

Level

1st Difference

Level

1st Difference

Intercept

Intercept & Trend

Intercept

Intercept

Intercept & Trend]

Intercept

2.619

-0.109

-3.880

2.619

-0.355

-3.885

GDPPC

(1.000)

(0.993)

(0.005)***

(1.000)

(0.986)

(0.005)***

-1.591

-1.434

-5.847

-1.553

-1.378

-5.858

GFCE

(0.477)

(0.834)

(0.000)***

(0.496)

(0.851)

(0.000)***

-2.373

-2.545

-7.025

-2.298

-2.545

-7.189

EDS

(0.156)

(0.306)

(0.000)***

(0.178)

(0.306)

(0.000)***

-2.256

-4.410

-9.185

-2.207

-4.235

-13.122

TR

(0.191)

(0.006)***

(0.000)***

(0.207)

(0.009)**

(0.000)***

-0.980

-1.595

-5.081

-0.980

-1.768

-5.062

AJ

(0.751)

(0.776)

(0.000)***

(0.751)

(0.700)

(0.000)***

-1.576

-2.392

-3.046

-1.042

-1.775

-3.080

BP

(0.485)

(0.378)

(0.040)**

(0.728)

(0.697)

(0.035)**

-0.226

-5.473

-7.754

-1.369

-5.872

-15.155

BW

(0.926)

(0.000)***

(0.000)***

(0.587)

(0.000)***

(0.000)***

-1.023

-2.315

-4.208

-1.002

-1.664

-3.083

CL

(0.735)

(0.416)

(0.002)***

(0.743)

(0.748)

(0.037)**

-1.109

-2.55

-3.818

-0.808

-1.856

-3.668

CCP

(0.702)

(0.303)

(0.006)***

(0.805)

(0.657)

(0.009)**

-0.965

-1.533

-6.074

-1.019

-1.763

-6.087

EFF

(0.756)

(0.800)

(0.000)***

(0.737)

(0.703)

(0.000)***

-1.458

-2.644

-4.193

-1.020

-1.766

-3.184

FACE

(0.543)

(0.264)

(0.002)***

(0.736)

(0.702)

(0.029)**

-1.561

-1.846

-3.378

-1.210

-1.305

-3.247

FAA

(0.492)

(0.662)

(0.018)**

(0.660)

(0.872)

(0.025)**

-0.850

-2.088

-6.083

-0.850

-2.131

-6.083

FMR

(0.793)

(0.536)

(0.000)***

(0.793)

(0.513)

(0.000)***

-0.983

-1.359

-5.551

-0.999

-1.467

-5.544

GE

(0.750)

(0.857)

(0.000)***

(0.744)

(0.824)

(0.000)***

-3.699

-3.646

-10.247

-3.655

-3.589

-12.273

HRP

(0.008)**

(0.039)**

(0.000)***

(0.009)**

(0.044)**

(0.000)***

Source: Secondary data (processed)

Note: () denotes t-statistics; ***, ** and * are significant levels of 1%, 5% and 10%, respectively.

The relationship analysis between fiscal indicators and civil liberties used VAR because it can describe the causality between variables. The interaction between variables was estimated using the Granger Causality Test (GCT). Piatek, Szarzec, and Pilc (2013) found that economic growth can be determined by the level of political freedom in 25 developing countries during 1990-2008. In addition, economic freedom

had a positive implication for economic growth.

The fiscal policy response to economic growth was estimated using a causality approach such as VAR. In particular, Mencinger, Aristovnik, and Verbič (2017) used a smooth transition VAR (STVAR) on EU and OECD countries during 1995-2014 and 1980-2014. Based on the findings, government expenditure can encourage

improvements in domestic economic conditions, especially in crisis conditions.

This study explained in full the relationship between indicators of fiscal policy and indicators of civil liberty on Per capita GDP in Indonesia during 1980-2018. This means, this study explored and extended empirical studies by Piatek, Szarzec, and Pilc (2013) and Mencinger, Aristovnik, and Verbič (2017).

Table 2 shows the results of VAR estimation on the relationship between indicators of fiscal policy and indicators of civil liberty in Indonesia on Per capita GDP (GDPPC) which were determined by lagged of GDPPC, tax ratio (TR), and several indicators of civil liberty such as Access to Justice (AJ) and barriers to parties (BP). The increase in tax ratio and barriers to parties had significant implications for the decline in Per capita GDP in Indonesia, while the

increase in access to justice contributed to the increase in Per capita GDP. Furthermore, the adjusted R-square and F-statistics were 99.82% and 12.65.90, respectively, which means the VAR model was correct and robust.

The Per capita GDP influenced a fiscal policy indicator namely external debt stock (EDS). This variable was influenced by lagged of EDS, AJ, civil liberties (CL), freedom of movement and residence (FMR), and gender equality (GE). If Per capita GDP increases, the external debt stock tends to decline. This means, to reduce external debt in Indonesia, Per capita GDP must be at an adequate level. How can this level of Per capita GDP be determined? More in-depth empirical studies such as threshold effect analysis and nonlinear modeling can be carried out. In addition, the CL and GE variables had positive implications on EDS while AJ and FMR had negative implications.

Table 2. Results of VAR Estimation

Variables

GDPPC

GFCE

EDS

TR

AJ

BP

BW

CL

-8.509

-940.078

9.964

373.057

[-

0.274

-0.071

0.120

-0.170

C

[-1.719]

1.212

[1.714]

[4.879]***

0.387]

[4.065]***

[-0.301]

[0.880]

[-2.215]**

GDPPC (-1)

[5.137]***

0.531

GFCE (-1)

[3.094]

0.598

EDS (-1)

[5.486]***

0.119

TR (-1)

[0.576]

-0.203

AJ (-1)

[-2.963]***

0.225

BP (-1)

[1.241]

0.170

BW (-1)

[0.567]

0.067

CL (-1) CCP (-1) EFF (-1) FACE (-1)

[0.747]

Variables

GDPPC

GFCE

EDS

TR

AJ

BP

BW

CL

FAA (-1) FMR (-1) GE (-1)

HRP (-1)

0.000

-0.026

0.002

-0.000

0.000

0.000

0.000

GDPPC

[0.351]

[-2.165]**

[0.561]

[-1.185]

[0.591]

[2.353]**

[0.364]

-1.882

-2.782

0.558

0.000

0.001

-0.002

0.004

GFCE

[-0.137]

[-1.102]

[0.969]

[0.038]

-0.001

[0.157]

[-0.471]

[1.895]

-1.214

-0.013

0.059

[-

0.000

0.000

0.000

EDS

[-1.203]

[-1.377]

[1.850]

4.489]***

[0.963]

[0.569]

[1.155]

-12.049

0.131

0.197

-0.001

0.000

-0.001

-0.000

TR

[-2.361]**

[2.099]**

[0.187]

-1289.075

[-1.263]

[0.179]

[-0.684]

[-0.049]

4229.256

4.930

[-

20.187

-0.900

0.255

0.375

AJ

[2.307]**

[0.348]

5.656]***

[0.427]

-16.107

[-1.595]

[0.876]

[1.816]

-1032.051

-0.500

148.578

[-

-0.081

0.061

0.092

BP

[-2.162]**

[-0.084]

[1.749]

0.762]

-12.428

[-1.244]

[0.470]

[1.269]

179.093

-14.512

205.661

[-

0.215

0.213

-0.059

BW

[0.199]

[-1.369]

[1.326]

0.335]

-65.284

[1.648]

[0.501]

[-0.374]

-2627.941

27.846

537.473

[-

0.445

0.900

-0.152

CL

[-1.935]

[1.776]

[2.088]**

1.173]

[2.400]**

[1.312]

[-0.386]

-18.092

-0.491

-838.395

7.068

198.754

[-

-0.051

[-

-0.107

0.001

CCP

[-1.391]

[0.992]

[1.940]

0.734]

[-0.621]

2.082]**

[-0.699]

[0.008]

318.007

-4.925

-41.569

15.420

0.078

0.196

-0.007

0.035

EFF

[1.324]

[-1.787]

[-0.933]

[1.493]

[2.508]**

[1.925]

[-0.104]

[0.875]

1991.157

-17.406

-168.988

42.441

0.280

1.050

-0.161

0.278

FACE

[1.895]

[-1.409]

[-0.887]

[0.880]

[1.996]*

[2.450]**

[-0.597]

[1.730]

-82.180

-10.498

-113.391

26.423

-0.042

-0.011

0.320

0.281

FAA

[-0.116]

[-1.188]

[-0.887]

[0.901]

[-0.408]

[-0.027]

[1.732]

[2.854]**

20.509

1.934

-83.877

12.169

-0.052

0.08

0.027

0.011

FMR

[0.118]

[0.927]

[-2.821]**

[1.462]

-4.440

[-2.169]**

[1.001]

[0.579]

[0.338]

-64.311

-10.499

255.648

[-

0.151

-0.306

0.090

0.143

GE

[-0.096]

[-1.427]

[2.405]**

0.166]

-0.101

[1.669]

[-1.065]

[0.564]

[1.403]

7.099

-0.873

-4.766

[-

-0.004

-0.032

-0.000

0.022

HRP

[0.101]

[-0.978]

[-0.357]

0.033]

[-0.380]

[-0.928]

[-0.007]

[2.12]**

Adj. R-square

0.9982

0.8911

0.9416

0.7333

0.9977

0.9917

0.9834

0.9990

F-statistics

1265.90***

19.42***

37.30***

7.19*

956.85***

269.87***

134.72***

2246.86***

Obervations

37

37

37

37

37

37

37

37

Source: Authors’ estimation

Note: [] denotes t-statistics;

***, ** and

* are significant at 1%

, 5% and 10%, respectively.

Continued…

Variables

CCP

EFF

FACE

FAA

FMR

GE

HRP

-0.453

-0.178

0.075

0.167

0.038

0.051

1.540

C

[-1.769]

[-0.370]

[0.784]

[1.191]

[0.065]

[0.294]

[1.084]

GDPPC (-1)

GFCE (-1)

EDS (-1)

TR (-1)

AJ (-1)

BP (-1)

BW (-1)

CL (-1)

.-0.127

CCP (-1)

[-1.015]

0.220

EFF (-1)

[1.592]

0.230

FACE (-1)

[2.569]**

0.304

FAA (-1)

[2.276]**

0.263

FMR (-1)

[1.352]

-0.036

GE (-1)

[-0.214]

0.063

HRP (-1)

0.000

-0.000

-0.000

-0.000

-0.000

0.000

[0.303]

0.000

GDPPC

[3.893]***

[-0.648]

[-1.133]

[-3.608]***

[-0.462]

[0.294]

[0.046]

0.013

-0.021

-0.006

-0.009

0.022

-0.008

-0.035

GFCE

[2.736]**

[-1.807]

[-2.395]**

[-2.529]**

[1.089]

[-1.602]

[-0.808]

0.000

-0.000

0.000

-0.000

-0.002

0.001

-0.000

EDS

[1.223]

[-0.478]

[0.986]

[-0.153]

[-2.576]**

[2.314]**

[-0.108]

-0.002

0.008

0.002

0.003

0.004

-0.000

-0.007

TR

[-0.689]

[1.549]

[1.494]

[1.795]

[0.572]

[-0.245]

[-0.447]

-0.279

2.936

0.430

0.018

-1.772

0.439

-0.146

AJ

[-0.483]

[2.790]**

[1.855]

[0.047]

[-1.301]

[1.006]

[-0.042]

-0.407

0.682

0.197

-0.007

0.504

-0.145

-0.812

BP

[-2.514]**

[1.737]

[2.642]**

[-0.051]

[0.815]

[-0.876]

[-0.581]

-0.353

-0.313

0.043

0.513

0.835

0.006

-0.324

BW

[-0.984]

[-0.368]

[0.231]

[2.008]**

[0.800]

[0.017]

[-0.126]

-0.016

0.800

0.446

0.780

0.484

0.817

7.603

CL

[-0.031]

[0.623]

[1.871]

[2.328]**

[0.281]

[1.757]

[2.081]**

0.895

0.322

0.501

0.490

-0.067

0.524

CCP

[1.959]*

[3.586]***

[4.137]***

[0.653]

[-0.341]

[0.300]

0.192

-0.145

-0.121

0.014

-0.032

-0.299

EFF

[2.045]**

[-4.331]***

[-1.866]

[0.049]

[-0.379]

[-0.406]

1.116

-2.433

-0.267

-0.571

-1.150

-0.332

-4.268

FACE

[2.934]***

[-3.274]***

[-2.018]**

[-2.073]**

[-0.611]

[-0.926]

[-1.433]

FAA

0.864

-0.895

-0.066

-0.184

-2.407

Variables

CCP

EFF

FACE

FAA

FMR

GE

HRP

FMR

[4.005]***

-0.010

[-0.139]

[-1.516]

0.032 [0.205]

0.046

[1.211]

0.0426 [0.8848]

[-0.075]

[-0.741]

0.162 [3.387]***

[-1.163]

0.166 [0.321]

GE

-0.061

[-0.237]

-0.098

[-0.177]

-0.253

[-2.091]

-0.147

[-0.816]

2.015

[2.914]***

-2.369

[-1.305]

HRP

0.013 [0.486]

-0.041

[-0.639]

-0.016

[-1.241]

-0.015

[-0.761]

0.018 [0.200]

-0.028

[-1.075]

Adj. R-square

0.9975

0.9863

0.9984

0.9963

0.9256

0.9833

0.1919

F-statistics

892.44***

162.40***

1417.13***

605.35***

28.98***

133.84***

1.53

Obervations

37

37

37

37

37

37

37

Source: Authors’ estimation

Note: [] denotes t-statistics; ***, ** and * are significant at 1%, 5% and 10%, respectively.

Several indicators of civil liberty had a significant relationship with Per capita GDP namely basic welfare (BW), civil society participation (CCP), and freedom of association and assembly (FAA). An increase in BW and CCP had implications for an increase in Per capita GDP while an increase in FAA had implications for a decrease in Per capita GDP. This means the government can maximize the fulfillment of basic needs and increase public participation in improving community welfare.

This study explored the relationship between indicators of civil liberty and indicators of fiscal policy. Based on the findings, AJ and FMR were determined by EDS negatively while GE was positively influenced. In addition, government expenditure (GFCE) had negative implications on freedom of academic and cultural expression (FACE) and the FAA had a positive contribution to CCP.

The GDP component can also be related to indicators of economic freedom in the long run (Sayari, Sari, and Hammoudeh, 2018). This is positively related to the services sector and industry while negatively related to agriculture. This means the pattern of the relationship between these variables is different in each economic

sector. On the other hand, this study only focused on aggregate data behavior namely GDP and indicators of civil liberty. In addition, community life in the past, such as in England, indicated a significant relationship between institutions and liberty, property, and state (Shepherd, 2018).

A low tax ratio during a crisis can control the deteriorating economic conditions. This is inseparable from the contribution of fiscal rule in encouraging fiscal consolidation (Dweck, Vianna, and Barbosa, 2019). Furthermore, Thanh, Hart, and Canh (2020) found that government expenditure in Vietnam tended not to be able to accelerate economic growth and there was a substitution between public spending and private investment. The finding is relevant to this study that there was no significant implication of government expenditure and external debt on Per capita GDP whereas the tax ratio had a significant and negative contribution. This means, an increase in tax ratio will reduce the community welfare.

Variance decomposition (Appendix C) showed that the GDPPC response was due to the shock of the GRCE variable which increased the GDPPC at the beginning to the middle of the period, then decreased until

the end of the period. Shock in the EDS variable increased slightly the GDPPC at the beginning of the period, then decreased in the middle of the period to the end of the period. Shock in the TR variable decreased the GDPPC at the beginning of the period and increased slowly and remained smooth at the end of the period. The AJ shock caused the GDPPC movement which slowly increased and tended to be stable until the end of the period. The shock variable BP caused the GDPPC decreased slowly, then increased slightly, and decreased until the end of the period. Shock BW made the GDPPC movement slightly decreased at the beginning of the period, then slowly increased and stabilized until the end of the period. The CL shock causes the GDPPC to increase and stabilize until the end of the period. GDPPC responded to the CCP shock with a decrease at the beginning of the period and a sharp increase until the end of the period.

Furthermore, GDPPC responded to the EFF shock with a decrease at the beginning of the period and tended to be very stable until the end of the period. FACE shock caused the GDPPC to decrease slightly at the beginning of the period to the middle of the period then experienced a slight increase until the end of the period. Shock FAA caused the GDPPC decreased at the beginning of the period and increased in the middle of the period to the end of the period. FMR shock caused the GDPPC to experience a steady increase from the beginning to the end of the period. Shock GE caused GDPPC to be increased slowly at the beginning of the period to the end of the period. The shock to the HRP variable caused the GDPPC to decrease slowly from the beginning of the period to the end of the period.

Based on the Granger causality test, there was no causality between indicators of

fiscal policy (government expenditure, external debt, and tax ratio) on Per capita GDP. This means that the level of public welfare was not significantly supported and driven by fiscal indicators during the study period. The explanation can be seen in Table 3.

On the other hand, the causality was proven in the relationship between the indicators of civil liberty and Per capita GDP. In addition, indicators of fiscal policy had a relationship with indicators of civil liberty.

In general, Per capita GDP had a causality towards indicators of civil liberty such as BP, AJ, CL, CCP, EFF, FACE, FAA, GE, and HE. This condition shows that people with relatively good welfare will be able to take advantage of their rights and obligations in their daily activities at the macro (national) level. Thus, the government should focus on increasing Per capita GDP to establish and fulfill civil liberty.

Fiscal indicators such as government expenditure had a causal effect on indicators of civil liberty. This means the fiscal allocation had significant implications on civil liberty for Indonesian society during the study period.

External debt stock and several indicators of civil liberty had a causality (bidirectional). The civil liberty indicators are BP, CL, CCP, EFF, FACE, FAA, GE, and HRP. This finding described that external debt policy had a significant contribution to civil liberties and vice versa.

Table 3. Summary of Granger Causality Test Results

Fiscal Policy and Per capita GDP

Civil Liberties and Per capita GDP

Fiscal Policy and Civil Liberties

No evidence (no causality)

  • •    GDPPC ÷ BP (sig. at 5%)

  • •    GDPPC ÷ AJ (sig. at 1%)

  • •    GDPPC ÷ CL (sig. at 1%)

  • •    GDPPC ÷ CCP (sig. at 1%)

  • •    GDPPC ÷ EFF (sig. at 1%)

  • •    GDPPC ÷ FACE (sig. at 1%)

  • •    GDPPC ÷ FAA (sig. at 1%)

  • •    GDPPC ÷ GE (sig. at 5%)

  • •    GDPPC ÷ HE (sig. at 5%)

  • •    GFCE ÷ AJ (sig. at 5%)

  • •    GFCE ÷ BP (sig. at 10%)

  • •    GFCE ÷ CL (sig. at 5%)

  • •    GFCE ÷ CCP (sig. at 5%)

  • •    GFCE ÷ EFF (sig. at 5%)

  • •    GFCE ÷ FACE (sig. at 5%)

  • •    GFCE ÷ FAA (sig. at 5%)

  • •    GFCE ÷ GE (sig. at 10%)

  • •    EDS ÷ AJ (sig. at 1%) and AJ ÷ EDS (sig. at 10%)

  • •    EDS ÷ BP (sig. at 10%) and BP ÷ EDS (sig. at 10%)

  • •    EDS ÷ CL (sig. at 1%) and CL ÷ EDS (sig. at 5%)

  • •    EDS ÷ CCP (sig. at 1%) and CCP ÷ EDS (sig. at 10%)

  • •    EDS ÷ EFF (sig. at 1%) and EFF ÷ EDS (sig. at 5%)

  • •    EDS ÷ FACE (sig. at 1%) and FACE ÷ EDS (sig. at 10%)

  • •    EDS ÷ FAA (sig. at 1%) and FAA ÷ EDS (sig. at 5%)

  • •    EDS ÷ GE (sig. at 5%)

  • •    EDS ÷ HRP (sig. at 10%)

  • •    TR ÷ AJ (sig. at 5%) and AJ ÷ TR (sig. at 1%)

  • •    TR ÷ FMR (sig. at 1%)

  • •    BP ÷ TR (sig. at 10%)

  • •    BW ÷ TR (sig. at 5%)

  • •    CL ÷ TR (sig. at 1%)

  • •    CCP ÷ TR (sig. at 1%)

  • •    EFF ÷ TR (sig. at 1%)

  • •    FACE ÷ TR (sig. at 1%)

  • •    FAA ÷ TR (sig. at 1%)

  • •    GE ÷ TR (sig. at 5%)

Source: Authors’ estimation

Note: the '÷' denotes causal (a direction).

The same thing happened to the tax ratio (TR) and several indicators of civil liberties. Bidirectional causality was evident in TR and AJ whereas TR had a unidirectional causality with FMR. Most of the causes were occurring on the indicator of civil liberty on TR. These indicators consist of BP, BW, CL, CCP, EFF, FACE, FAA, and GE. Thus, the government should increase the facilitation and supervision of the civil liberties of the Indonesian people in order to improve and encourage an increase in the tax ratio.

This study conducted robustness checking with Bayesian VAR (BVAR) estimation. BVAR was used in economic and financial analysis (Miranda-Agrippino and Ricco, 2018). BVAR was used to estimate economic theories and does not emphasize hypothesis formulations even though the estimation results can provide robust parameters. In addition, BVAR was correct to estimate empirical models with large dimensions.

Appendix D shows the results of Bayesian VAR (BVAR) estimation on the relationship between fiscal policy and civil liberties on Per capita GDP in Indonesia during 1980-2018. Based on the empirical finding, Per capita GDP was determined by the lagged of GDPPC, EDS, BW, FAA, and HRP. The positive relationship was between Per capita GDP and lagged Per capita GDP and BW had a negative relationship which was determined by EDS, FAA, and HRP. This explains that high basic welfare had a significant increase in the increase in Per capita GDP in Indonesia.

Per capita GDP has a negative implication on external debt stock (EDS). This means, the higher the welfare of society, the government can reduce the level of external debt.

Furthermore, Per capita GDP has a significant influence on several indicators of civil liberty namely BW, CCP, and FAA. BW and CCP were positively determined by Per capita GDP. Thus, the government can encourage the improvement of basic welfare and civil society participation to boost Per capita GDP. Conversely, freedom of association and assembly (FAA) was negatively affected by Per capita GDP. This describes that the higher the welfare, the less freedom of society in the association is.

Furthermore, statistically adjusted R-square and F-statistics showed that the Bayesian VAR model was fit and significant. This means robustness checking in BVAR estimation can explain the study objectives and robust.

CONCLUSION AND POLICY IMPLICATION

Several empirical findings confirmed the relationship between indicators of fiscal policy and indicators of democracy or civil liberty on economic growth. This study estimated the relationship between indicators of fiscal policy and indicators of civil liberty on Per capita GDP in Indonesia during 1980-2018. This study used VAR and Granger Causality estimations.

The VAR estimation described the significant relationship between the tax ratio and Per capita GDP. This condition confirmed that the fiscal policy can be an instrument for measuring community welfare in Indonesia. Barriers to parties as one of the civil liberty indicators is also an obstacle to increasing Per capita GDP. Meanwhile, other civil liberty indicators such as access to justice had a significant contribution to encourage an increase in Per capita GDP. Another finding confirmed that Per capita GDP had a significant influence on external debt stock.

Based on the Granger causality test, fiscal policy and Per capita GDP had no causality. However, causality occurred between civil liberties and Per capita GDP, and fiscal policy and civil liberties.

The Indonesian government can optimize the allocation of external debt for productive national strategic programs, improve public welfare by improving indicators of civil liberties so that people will actively and productively participate in the development and the quality of life in society, politics, and association, including in education. The government also needs to maintain an efficient and productive allocation of state and regional budget expenditures. Integrated policies and efficient bureaucracy across sectors and regions will make it easier to achieve public welfare. Community welfare is expected to reduce the external debt.

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