Eduvest ï¿½ Journal of Universal Studies Volume 4 Number 12, ï¿½December, 2024 p- ISSN 2775-3735- e-ISSN 2775-3727

ANALYSIS OF THE INTERACTION AND CONTRIBUTIONï¿½ OF THE ISLAMIC HUMAN DEVELOPMENT INDEXï¿½ (IHDI) AND TOTAL ISLAMIC BANKING ASSETS IN INDONESIA'S ECONOMIC GROWTH WITH AN AUTO REGRESSION (VAR) APPROACH
Muhammad Agung Nugraha, Imsar Fakultas Ekonomi dan Bisnis Islam, Universitas Islam Negeri Sumatera Utara Indonesia Email: a[email protected]
ABSTRACT
This study investigates the interaction and contribution of the Islamic Human Development Index (IHDI) and total Islamic banking assets to Indonesia's economic growth during the period 2010-2023 using the Auto Regression (VAR) approach. The results show that both independent variables have a positive and significant influence on economic growth, confirming the important role of Sharia economics in national economic development. This research highlights the need for the integration of Islamic values in human development and the financial sector to encourage sustainable economic growth. Suggestions for future research include increasing the integration of IHDI in public policy and further development of the Islamic financial sector as a driver of the national economy. These findings provide new insights for policymakers and practitioners in designing inclusive and sustainable economic strategies.
KEYWORDS	Islamic Human Development Index (IHDI), Islamic Banking, Vector Autoregression (VAR)
	*This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International*

INTRODUCTION

IHDI emerged as an alternative to measuring human development by incorporating Islamic values that include spiritual dimensions as well as aspects of social justice (Mahri & Nurasyiah, 2020). Different from the traditional Human Development Index (HDI), IHDI offers a more holistic perspective in assessing human development, highlighting the importance of a balance between material and spiritual (Srivastava & Misra, 2024). This is especially relevant in countries with a majority Muslim population, such as Indonesia (Arwendi & Himmati, 2024). The Islamic Human Development Index (IHDI) is designed to measure the level of human development in society by incorporating the spiritual dimension and Islamic values into traditional indicators of human development, such as education, health, and income (Avdukic & Asutay, 2024). Different from the Human Development Index (HDI) issued by UNDP, IHDI adds factors such as economic justice, equality, and environmental sustainability, which are considered relevant to Islamic principles about human welfare and social justice (Mahri & Nurasyiah, 2020). Related studies have explored the influence of digital economy interaction, economic openness, IHDI, and investment on Indonesia's GDP growth (Imsar et al., 2023), as well as the impact of poverty, economic growth, and government spending on IHDI in Indonesia (Dalimunthe & Imsar, 2023), I-HDI contains several things based on Maqhasid Sharia. Maqhasid Syariah is a description of the components that support the benefit of the ummah or common welfare. The components in Maqhasid Sharia consist of five points, namely Hifz Ad-Diin (Religious Protection), Hifz An-Nafs (Life Protection), Hifz Al-'Aql (Protection of Intellect), Hifz Nasl (Protection of Descendants), and Hifz Al-Maal (Protection of Property). The use of components representing these five aspects in the calculation of I-HDI is assumed to explain the influence of Maqhasid Sharia in creating welfare (Rahim et al., 2022). I-HDI can be influenced by many factors related to economic development.

The Islamic finance sector in Indonesia has experienced rapid development in the last decade, with total Islamic banking assets showing significant growth (Mawardi et al., 2024). The uniqueness of sharia principles that prioritize justice and transparency provides an alternative for the public in financial transactions. This growth reflects the increase in public trust and the great potential of Islamic banking in supporting national economic activities. (Ali, 2020) The growth of Islamic banking assets shows great potential in supporting the Indonesian economy. Policies must be designed to strengthen the infrastructure of Islamic finance, including increasing the capacity of Islamic financial institutions, simplifying procedures for Islamic financial products, and encouraging innovation (Rabbani et al., 2021). As mentioned in the Quran.

وَاَحَلَّ اللّٰهُ الْبَيْعَ وَحَرَّمَ الرِّبٰواۗ فَمَنْ جَاۤءَهٗ مَوْعِظَةٌ مِّنْ رَّبِّهٖ فَانْتَهٰى فَلَهٗ مَا سَلَفَۗ وَاَمْرُهٗٓ اِلَى اللّٰهِ ۗ وَمَنْ عَادَ فَاُولٰۤىِٕكَ اَصْحٰبُ النَّارِ ۚ هُمْ فِيْهَا خٰلِدُوْنَ

Meaning: Allah has legalized buying and selling and forbids usury. Whoever has given him a warning from his Lord (concerning usury) stops so that what he has obtained in the past belongs to him, and his business is up to Allah. Whoever repeats (the transaction of usury), they are the inhabitants of hell. They remain in it. (QS. Al-Baqarah: 275)

The verse "Allah legalizes buying and selling and prohibits usury" (QS. Al-Baqarah: 275) has a significant correlation with the implications of the analysis on the development of Islamic economic and financial policies in Indonesia. This correlation can be explained through several key aspects of the Development of Sharia Financial Products; this verse encourages the development and promotion of Islamic financial products that do not involve usury. Islamic economic and financial policies in Indonesia, which are based on Islamic principles, must encourage innovation of products and services in accordance with Sharia law, offering alternatives for people who want to transact without engaging in usury. The prohibition of riba emphasizes transparency, fairness, and ethics in economic transactions. The policies developed must ensure that Islamic finance practices in Indonesia adhere to these principles, encouraging a fairer and ethical financial system, which in turn can support inclusive and sustainable economic growth.

Economic growth is one of the fundamental indicators in assessing the progress and welfare of a country (Nowak & Kokocińska, 2024). Especially in Indonesia, the largest economy in Southeast Asia, economic growth signals not only an increase in national production capacity but also an improvement in the quality of life of the people (Hardi et al., 2024). Sustainable economic growth is considered crucial to achieving national development targets and reducing poverty (Abdulkareem et al., 2023). From a theoretical perspective, there is a close interaction between economic growth, the Islamic financial sector, and IHDI (Lestari & Arumi, 2024). Sharia economic principles applied in Islamic banking, such as the avoidance of riba and the emphasis on fair transactions, not only support economic growth but also inclusive social development (Iqbal et al., 2024). IHDI, with its holistic approach, offers a framework for assessing how economic growth can create broader well-being, including spiritual satisfaction and social justice.

The purpose of this study is to explore the dynamics and contribution of the Islamic Human Development Index (IHDI) as well as the total assets of Islamic banking in the context of Indonesia's economic growth. Focusing on the analysis of the interaction between IHDI, Islamic banking assets, and national economic dynamics, this study seeks to identify how Islamic financial practices and Islamic values-based development principles contribute to the improvement of Indonesia's economy, providing new insights and in-depth insights into the causal relationship between these variables.

The formulation of this problem explores how the interaction between the Islamic Human Development Index (IHDI) and the total assets of Islamic banking affects Indonesia's economic growth, with the aim of understanding the dynamics between the implementation of Islamic values, the existence of the Islamic financial sector, and its contribution to the improvement of the national economy. This study aims to identify and measure the significance of the impact of these two variables on Indonesia's GDP, providing insight into their potential to support inclusive and sustainable economic development (Arwendi & Himmati, 2024); Fahmi, 2019).

This research focuses on the period 2010 to 2023 to understand Indonesia's economic growth trends, with the study limited to three main variables: IHDI, total Islamic banking assets, and economic growth. This approach ensures in-depth and relevant analysis, avoiding the complexity of external variables. The methodological discussion of this study was limited to the use of the Auto Regression (VAR) approach. This approach was chosen because of its ability to describe the dynamic relationships between variables in the economic system, providing a strong analytical foundation for achieving research objectives (Baumeister & Hamilton, 2024).

RESEARCH METHODS

This study adopts a quantitative methodology to examine the interaction and contribution of the Islamic Human Development Index (IHDI) and Total Islamic Banking Assets to Economic Growth in Indonesia during the period 2010-2023. This approach aims to gain a deeper understanding of the dynamics of the Sharia economy and its impact on the national economy as a whole. This research is based on the philosophy of positivism, where hypothesis testing is carried out through the collection of empirical data and statistical analysis to obtain objective results.

This study uses secondary data that is a time series or time series over a period of 14 years, from 2010 to 2023. The source of Islamic Human Development Index (IHDI)ï¿½ data is taken from the official publications of research institutions or government bodies that study human development from a sharia perspective. Total Sharia Banking Assets The data is obtained from the annual reports of Bank Indonesia (BI) and the Financial Services Authority (OJK), which reflect the total value of assets held by Islamic banks in Indonesia. Economic Growth Indonesia's Gross Domestic Product (GDP) data on the basis of constant prices is taken from the website of the Central Statistics Agency (BPS).

Given the nature of the data used, this study did not apply a sampling technique because it involved the analysis of the overall time sequence data available for the research variables during the research period. Dependent Variable of Economic Growth (measured through GDP on a constant price basis). Independent Variables of Islamic Human Development Index (IHDI) and Total Sharia Banking Assets.ï¿½ï¿½

Sims, C. A. (1980). "Macroeconomics and Reality." Econometrica Introduces the use of VAR models in econometrics to analyze dynamic relationships between economic variables. Data analysis was carried out using the Vector AutoRegression (VAR) method, which allowed the study to capture the dynamic relationship between the variables studied and evaluate the contribution of each independent variable to the dependent variable. The VAR method was chosen because of its superiority in analyzing the relationship between variables in a complex and interdependent economic system. Prior to performing the VAR analysis, stationarity testing will be performed on all data series using the Dickey-Fuller Augmented Unit Root Test (ADF) to ensure that all variables in the model are stationary. If needed, data transformation will be carried out to achieve stationarity. Further analysis includes VAR model estimation, Granger causality test, and analysis of the breakeven response function to assess the impact of changes in the IHDI and Total Islamic Banking Assets on Indonesia's Economic Growth. This study will also apply Predictive Variance Decomposition to explore the relative contribution of each independent variable to the variability of independent variables. The analysis technique used is the Vector Error Correction Model (VECM) approach to see the relationship between variables that represent indicators of economic growth. Engle and Granger first popularized the VECM analysis method. Correcting the short-term imbalance against the long-term. So, VECM can be used to see short-term and long-term relationships from time-lapse data. VECM is a Vector Auto Regression (VAR) analysis designed to be used on non-stationary data that is known to have a cointegration relationship; in other words, VECM can be said to be a form of restricted VAR (Saputra and Sukmawati 2021).

Vector Error Correction Model (VECM) analysis is an important procedure in econometric research for time series data. Here are the recommended analytical steps:

Stationary Test

The key in time series analysis is to ensure that the data is stationary. The Augmented Dickey-Fuller (ADF) method is often used to test for the presence of a root unit, which is an indicator of non-stationarity (Gujarati & Porter, 2009).

Optimal Lag Selection

Determining the right amount of lag is critical in building an accurate VAR/VECM model, usually selected based on criteria such as AIC and BIC (Schwarz, 1978).

VAR Stability Test

A VAR model must be stable to be valid. This is usually checked through the eigenvalues of the VAR matrix (Lï¿½tkepohl, 2005).

Granger Causality Test

This method evaluates the cause-and-effect relationship between variables, which is important for understanding economic dynamics (Granger, 1969).

Cointegration Test

The Johansen test is a standard method to identify the cointegration relationship between non-stationary variables (Johansen, 1988).

Impulse Response Function (IRF)

IRF is used to see how much impact one variable has on another variable when the first variable receives a single 'shock' (Sims, 1980).

Predicted Variance Decomposition (FEVD)

FEVD helps to outline how much variability in each variable can be explained by innovations in other variables in the system (Lï¿½tkepohl, 2005).

Research using VECM allows for a more comprehensive analysis of long-term relationships and short-term adjustments between economic variables (Enders, 2004)."

RESULT AND DISCUSSION

Stationary Test

Data Stationary Test Results with ADF Test

Table 1

Variable	Test at Level	ADF Statistics	Probability
IHDI	Level	1.665794	0.9996
APPS	Level	-1.309490	0.6245
PE	Level	0.702567	0.9919

The results of the data stationarity test using the Augmented Dickey-Fuller test (ADF) at the level for the IHDI, APS, and PE variables showed that these three-time series were not stationary at the level because the probability value was greater than 0.05. This indicates that the data requires further differentiation to achieve stationarity.

Table of stationarity test results with the Dickey-Fuller Augmented Test (ADF) at the first level (First Difference) for IHDI, APS, and PE variables. This stationarity test was conducted to ensure that the data used in the analysis were stationary, which is a prerequisite for time series regression analysis using VAR and VECM models.

Optimal Lag Selection

The results of the analysis of the optimal lag selection for the VAR model based on the information criteria of Akaike (AIC), Schwarz (SC), and Hannan-Quinn (HQ) from the data presented show that the second lag (Lag 2) is the optimal lag for the model. The lowest AIC value in Lag 2 indicates that in this lag, the model is able to provide the best estimate with the least amount of information lost. The following is a table of optimal lag selection:

Table 2

Lag	LogL	LR	FPE	AIC	SC
0	-4497.212	NA	9.83e+19	54.54802	54.60450
1	-3204.000	2523.723	1.71e+13	38.98181	39.20770
2	-3152.984	97.70246	1.03e+13	38.47254	*38.86784
3	-3135.257	*33.30546	*9.23e+12	*38.36675	38.93147

Taking into account the value of the information criterion, the lag chosen is Lag 2 because it has an SC value that is marked as the most optimal value. In the context of VAR and VECM models, choosing the right lag is crucial because it determines how the model will respond to changes in the variables included in the model. Based on the selected lag, further analysis will be carried out using the VAR model in the second leg, followed by the steps of Granger causality analysis and cointegration test to determine the long-term relationship between the variables studied.

VAR Stability Test

VAR Stability Test Results Table

Table 3

Variable	Characteristic Roots	Modulus
PE	0.996553	0.996553
IHDI	0.823215	0.823215
APPS	0.578530	0.587340
...	...	...
Final roots	-0.344808	0.346517

Note: A characteristic root that has a modulus of less than 1 indicates that the VAR model is stable.

The results of the VAR Stability Test show that the model we analyzed has a characteristic root with a modulus of less than 1. This shows that the VAR model we are using is stable. Stability in VAR models is an important condition to ensure that the results produced can be trusted to make predictions. In the context of VAR, if all the characteristic roots of the autoregressive matrix determinants have a modulus of less than one, then the dynamic system will return to long-term equilibrium after experiencing a disturbance.

An example of a calculated characteristic root from the estimated model gives a value of 0.996553 for the PE variable, indicating that this variable is stable. This also applies to IHDI and APS with modulus values of 0.823215 and 0.587340, respectively. Therefore, the estimated VAR model has stable dynamic properties and can be used for further analysis, such as the Impulse Response Function or Forecast Error Variance Decomposition, to understand how variables in the system react to changes in the model.

Granger Causality Test

The results of the Granger Causality Test showed that only the APS variable did not cause IHDI significantly, with an F-statistic value of 4.09475 and a probability value (Prob.) of 0.0184. This means that the APS Granger variable causes IHDI. However, IHDI is not Granger-causing APS with an F-Statistic value of 1.31032 and a probability value of 0.2726, which is higher than the significance level of 0.05, so the causality relationship is not significant.

The following is a table of the results of the Granger Causality Test based on the data provided:

Table 4

Null Hypothesis	Observations	F-Statistic	Probability
APS does not Granger Cause IHDI	165	4.09475	0.0184
IHDI does not Granger Cause APS	165	1.31032	0.2726

Based on these results, it can be concluded that changes in APS affect IHDI in the sample studied. However, there is not enough evidence to state that the changes in IHDI affect APS.

Cointegration Test

The Stationary at Level test is the first step in a time series analysis to determine if the data has a root unit, which is an indication that the data is not stationary. This testing is important because most econometric models require static data. The Dickey-Fuller Augmented Test (ADF) is one of the methods used to test the null hypothesis that the series has a root unit (not stationary).

The results of the ADF Test at Level for IHDI show that the t-statistic is 1.665794 with a p-value of 0.9996. Since the p-value is greater than 0.05 and the t-statistically greater than the critical values at the significance levels of 1%, 5%, and 10%, we do not reject the null hypothesis and conclude that the IHDI is at a non-stationary level.

The results of the ADF Test at Level for APS show that the t-statistic is -1.309490 with a p-value of 0.6245. Since p-values are greater than 0.05 and t-statistically greater than critical values at the same significance level, we do not reject the null hypothesis and conclude that APS is at a non-stationary level.

The results of the ADF Test at the Level for PE show that the t-statistic is 0.702567 with a p value of 0.9919. With a p-value greater than 0.05 and a t-statistically greater than the critical values at the significance level already mentioned, we do not reject the null hypothesis and conclude that PE at the level is not stationary.

Furthermore, the results of the stationarity test at the First Difference or Second Difference will usually indicate whether the variable becomes stationary after the first or second differentiation. This is the next step that must be done if the variable at the level is not stationary.

The following is a table of the results of the Stationary Test with the ADF Test at the Level for IHDI, APS, and PE:

Table 5

Variable	t-Statistic	Prob.
IHDI	1.665794	0.9996
APPS	-1.309490	0.6245
PE	0.702567	0.9919

Based on the above results, we can conclude that all variables at the level are not stationary and need to be differentiated to achieve stationarity.

Impulse Response Function (IRF)

Table 6

Era	IHDI's response to PE	IHDI's response to IHDI	IHDI's Response to APS
1	Close to 0	Positive	0
2	Increase	Increase	Increase
...	...	...	...
100	Positive (Long-term)	Positive (Stable)	Positive (Long-term)

Period: This column represents the time after the shock in which the response was observed.

IHDI's response to PE: This column shows how IHDI responds to a single unit shock in PE over time.

IHDI's response to IHDI: This column shows how IHDI reacts to shocks within itself, indicating self-recovery or self-stability throughout the period.

IHDI's response to APS: This column shows how IHDI responded to shocks within APS, describing the impact of APS on IHDI over time.

Predicted Variance Decomposition (FEVD)

Table 7

Variable	Period	IHDI Variant (%)	APS Variant (%)
IHDI	1	100.0000	0.0000
	2	99.9691	0.0071
	3	99.9381	0.0141
APPS	1	0.0000	100.0000
	2	0.0053	99.9945
	3	0.0105	99.9890
PE	1	0.0000	0.0000
	2	0.0004	0.0000
	3	0.0008	0.0000

IHDI: The IHDI variance is heavily dominated by its own past information, with very little contribution from APS and PE in predicting future variance.

APS: Similar to IHDI, APS is also heavily influenced by its historical data, with minimal contributions from IHDI and PE.

PE: The PE variance is almost entirely explained by its own historical values, with very small contributions from IHDI and APS.

This explanation shows that each variable tends to be influenced by its own historical values more than other variables in the short term. This shows the relative independence between variables in the context of their prediction variance.

This study explores the dynamic interaction between the Islamic Human Development Index (IHDI), total Islamic banking assets, and economic growth in Indonesia, presenting an in-depth perspective on the potential of the Islamic economic sector in driving the wheels of the national economy. Through the use of data covering the period from 2010 to 2023, this analysis not only highlights the direct contribution of the Islamic finance sector and IHDI to the economy but also reveals how the integration of Sharia principles in human development and operational aspects of Islamic banking contributes to the broader economic ecosystem.

In the era of globalization that demands inclusivity and sustainability, the results of this study reaffirm the relevance and urgency of the application of Sharia ethical values in economic and financial practices. The growth of Islamic banking assets in Indonesia, which reflects the high level of public interest and trust in the Islamic financial system, is an indication of a significant evolution in financial preferences that are not solely profit-oriented but also in aspects of justice and social balance.

The use of the Islamic Human Development Index (IHDI) as a human development parameter underscores the importance of including spiritual dimensions and ethical values in conventional development parameters. Through IHDI, which integrates aspects of justice, equality, and shared well-being, the research offers a new perspective in assessing socioeconomic progress that is in line with Islamic principles, marking an important step in efforts to create a more inclusive and holistic development model.

Analysis using the Auto Regression (VAR) approach revealed that IHDI and total Islamic banking assets play a positive and significant role in Indonesia's economic growth, confirming these two variables as key elements in the country's economic structure. Therefore, this study not only contributes to the academic literature on sharia economics and its impact on economic growth but also offers practical recommendations for policymakers and practitioners in formulating sustainable and inclusive economic strategies.

Overall, the findings of this study affirm the strategic position of sharia economics in Indonesia's economic architecture, showing that the integration of sharia principles in economic and social development is not only beneficial in increasing economic growth but also in creating a more equitable and sustainable society.

CONCLUSION

The conclusion of this study shows that the Islamic Human Development Index (IHDI) and total Islamic banking assets make a positive contribution to Indonesia's economic growth in the period from 2010 to 2023. It was found that both not only support economic growth directly, but also enrich the development dimension with the integration of Islamic values that focus on justice, common welfare, and holistic human development.

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