Impact of Digitalization on Sustainable Development: A Comparative Analysis of Developed and Developing Economies

Abstract

The role of digitization in promoting Sustainable Development (SD), as a key topic in recent scientific research, remains a subject of debate. In order to investigate this, a panel dataset covering 28 developed and 27 developing countries from 2000 to 2020 was used to assess the impact of the digital economy on SD. The findings show that while digital indicators have varying effects on the Human Development Index (HDI), factors like mobile subscriptions, internet users, and broadband access significantly influence human development, with impacts differing by country’s development level. These results offer nuanced insights for policymakers, highlighting that digital transformation must be supported by adequate infrastructure, institutional quality, and inclusive policies to effectively contribute to sustainable development in developing countries.

1. Introduction

The concept of the digital economy, an economic system driven by digital technologies, especially the Internet, online platforms, and electronic transactions, was first introduced by Tapscott (1997). Since then, the digital economy has experienced significant global expansion, largely propelled by advancements in Information and Communication Technology (ICT). As noted by Myovella et al. (2019), this digital transformation has fundamentally altered how individuals, consumers, and businesses interact, work, and operate worldwide. The accelerated evolution of core digital technologies, including cloud computing, big data, artificial intelligence, and 5G, is poised to further redefine the future of employment, skill requirements, and business models. These technologies represent some of the most important digital indicators, quantitative measures used to evaluate the extent of digital access and use in a country, such as the number of internet users, mobile phone subscriptions, and broadband connections. The digital economy has rapidly emerged and expanded with the advent of new technologies (Pan et al., 2022), driving profound changes in societies and businesses, leading them to become predominantly digital.

The digital economy transcends conventional temporal and spatial boundaries, enabling the intensified integration and efficient coordination of production factors (Miao, 2021; Gao et al., 2022; Liang & Tan, 2024). As a result, economic digitalization promotes a transformation in production models and fosters the restructuring of innovation systems. In this context, innovation emerges as a critical driver, exerting a direct influence on production dynamics and the broader socio-economic development of nations (Cardona et al., 2013; Lopes et al., 2022)

Furthermore, digital products and their distribution have advanced rapidly, encompassing sectors such as industry, the environment, healthcare, education, government, financial services, and leisure. It is evident that digitalization has enhanced the productivity of traditional manufacturing, reshaped industrial structures, and fostered more efficient markets (Ren et al., 2022). In essence, the digital economy, propelled by digital technologies and data, is increasingly infiltrating all dimensions of society, including critical domains linked to sustainable development.

This perspective aligns with the foundational concept of sustainable development, originally articulated in the early 20th century as development that satisfies present needs without jeopardizing the ability of future generations to meet their own (Stoddart, 2011). According to the Triple Bottom Line framework, sustainable development comprises three interdependent dimensions economic, social, and environmental all of which are being increasingly shaped by the transformative impact of the digital economy (Elkington, 1997).

Achieving sustainable development is one of the world’s most critical objectives, as emphasized by the United Nations (UN) in 2015. This goal was formally reinforced when global leaders united to pledge their commitment to ensuring the rights and well-being of all people on a healthy, prosperous planet, with the target of reaching this vision by 2030.

The United Nations Member States have agreed on a framework to guide countries in shaping both local and global development strategies while establishing a series of Sustainable Development Goals (SDGs) to assess sustainability from a triple-bottom-line perspective, as follows: environmental, social, and economic sustainability (United Nations, 2015; Ripple et al., 2017). Since the 2015 summit that introduced the 2030 Agenda, 17 primary goals and 169 sub-goals have been set to drive sustainable development (Naidoo & Fisher, 2020), with various sustainability measures developed and utilized by researchers and policymakers alike.

The existing corpus of research has predominantly focused on index construction of sustainable development and its prominent influencing factors. Researchers have identified several indicators and indices, notably, the Human Development Index (Chen et al., 2020; Mangaraj & Aparajita, 2020), Environmental Performance Index (Wolf et al., 2022), Green Growth Index, Adjusted Net Savings (Gale, 2018), etc. It is worth mentioning that the current study is particularly concerned with the Human Development Index (HDI), endorsed by the United Nations as a crucial indicator of sustainable development due to its ability to capture key dimensions of human well-being. In other words, the HDI is provided as a proxy for sustainable development. The HDI is a composite indicator which measures a country’s average achievements in the following three key dimensions: life expectancy (health), education level (knowledge), and gross national income per capita (standard of living). A higher HDI score reflects a higher level of human well-being and socio-economic progress.

As nations worldwide confront a range of pressing global challenges, such as pandemics (e.g., COVID-19), climate change, biodiversity loss, socio-economic inequality, and environmental degradation, the role of digital technologies has become increasingly pivotal. The advancement of a digital economy is, therefore, poised to play a crucial role in shaping the sustainable development of both economies and societies. Moreover, the quality of institutions, especially corruption, remains a critical factor influencing development outcomes and may interact with digitalization in complex ways.

Accordingly, it could be stated that both sustainable development and the digital economy are priorities of countries across the world. Furthermore, studying the impact of digitalization on a country’s sustainable development seems to be a relevant and important topic for shaping digital development strategies and improving their sustainability at the national and global levels.

Although digitization is widely discussed in relation to Sustainable Development (SD), existing research offers mixed findings and often lacks comparative analysis across development levels. This study addresses that gap by analyzing a panel of 55 countries from 2000 to 2020, using the Human Development Index (HDI) as a proxy for SD. Motivated by the need to clarify digitalization’s role in development, this study examines the impact of key digital indicators, such as internet use, mobile subscriptions, and broadband access. It contributes to theory by offering a cross-contextual assessment of digital development and to policy and practice by informing more targeted, development-sensitive digital strategies.

The present study explores the relationship between the digital economy and sustainable development through a comparative analysis of developed and developing countries. It seeks to answer the following two primary questions: (1) Does the digital economy have a significant influence on sustainable development, as measured by the Human Development Index (HDI)? (2) Are there notable differences in the impact of the digital economy on sustainable development across different country clusters? This investigation is guided by two key hypotheses. The first hypothesis (H1) posits that the digital economy has a direct and positive effect on sustainable human development. The second hypothesis (H2) suggests that corruption negatively impacts the Sustainable Human Development Index.

The structure of this paper is as follows: Section 1 provides an overview of the research context and the significance of the topic. Section 2 presents a review of the relevant literature. Section 3 details the data and methodology employed in the study. The key empirical findings and a discussion of the results are discussed in Section 4. Finally, Section 5 concludes the paper, offering policy implications and suggestions for future research.

2. Literature Review and Development of Hypotheses

2.1. Positive Relationship Between Digitalization and Sustainable Development

Numerous studies have focused on the effect of digitization on sustainable development. In the current section, we summarize the main recent studies that dealt with the effect of digital economy on sustainable development. Gupta and Rhyner (2022) introduced the Digitainability Assessment Framework (DAF), designed to evaluate how digital technologies influence progress toward the SDGs. Grounded in the Theory of Change, the DAF provides a multidimensional analysis—technical, social, and environmental—offering strategic insights for optimizing digital innovations to advance sustainable development. Many scholars believe that digitalization plays a key role in advancing sustainable development, mainly reflected through three main dimensions. From an economic standpoint, Myovella et al. (2019) explored how the digital economy influences economic growth. By applying the Generalized Method of Moments (GMM) to data from 41 sub-Saharan African and 33 OECD countries between 2006 and 2016, their study concluded that digital innovations positively influence economic growth.

Furthermore, a study by Bahrini and Qaffas (2019) analyzed the relationship between the digital economy and economic development in the MENA region from 2007 to 2016, employing panel data and the GMM approach. Their findings suggest that key components of the digital economy, notably mobile phones, internet users, and broadband subscriptions, play a significant role in promoting economic growth.

In turn, Fernández-Portillo et al. (2020) examined how the digital economy influences the economic growth of the European community and concluded that digitalization plays a significant role in boosting economic growth. In the same vein, Solomon and van Klyton (2020) investigated the impact of the digital economy on economic growth in 39 African countries between 2012 and 2016. These authors empirically evidenced that the use of ICT has a positive effect on national wealth. Zhang et al. (2022) applied an empirical study using a panel-data regression approach to examine the effect of digital economy on economic growth in 31 countries from 2009 to 2019 along the “Belt and Road” and prior to the onset of COVID-19. Their findings show that the digital economy has a significant and positive impact on economic growth across the countries analyzed, where the development of digital economy was uneven. Second, from a social perspective, Imran et al. (2022) employed panel regression modeling to explore the direct effect of Digital Economics and Social Indicators (DESI) on the Sustainable Development Goals Index (SDGI) across European Union countries.

The DESI is expressed in five key dimensions, notably, connectivity, human capital, internet usage, digital technology integration, and digital public services. The results of their study show that the DESI dimensions affect the SGDI in distinct ways. It is worth noting that the distinct effects of connectivity, human capital, and internet usage on the SGDI are more important compared to the adoption of digital technologies and the availability of digital public services.

In addition, contrary to the academic literature, the authors concluded that the universal idea of the positive impact of digital economy is not always reliable. For instance, on the “connectivity” dimension of the DESI, the authors indicated that only two variables (4G coverage and mobile broadband take-up) have positively influenced the SGDI. However, the overall adoption of fixed-broadband and very-high-capacity fixed networks have a significant and negative impact on the SGDI. Furthermore, the number of ICT professionals and graduates, which refers to the human capital dimension, was a significant variable but had a negative impact on the SDGI.

Furthermore, Moussa et al. (2024) examined the effect of digital economy on sustainable development, measured through the Human Development Index, across 28 developed and 25 developing nations over the period 1990–2022. Using the Within and Generalized Least Squares (GLS) methods (Wooldridge, 2010; Greene, 2018) and utilizing the Hausman test (Hausman, 1978) to account for individual effects, their results indicate that the digital economy plays a significant and positive role in promoting sustainable development. In particular, mobile technologies have substantial positive impacts on sustainable development in developing countries, while these impacts are less resilient in developed countries. In this sense, the realm of the Internet has positively and significantly impacted the Human Development Index. Third, from an environmental perspective, Moussa et al. (2024) also examined the empirical link between environmental sustainability and digitalization. They argue that the development of the digital economy contributes more effectively and significantly to improving environmental performance in developed countries compared to developing ones. Yang et al. (2022) conducted a study based on China’s provincial panel data, demonstrating that the digital economy promoted green environmental protection and ecological sustainability and consequently, contributed to the achievement of the Sustainable Development Goals. Using panel data from 12 provinces, in less developed regions of western China, over the period 2011–2022, Feng et al. (2025) employed double-machine-learning method to objectively assess the impact of the digital economy on sustainable development in these regions. Their findings reveal that key components of the digital economy, mainly digital infrastructure, industrialization, and innovation, play a significant role in promoting sustainable development in less developed regions of western China.

Extending this line of inquiry, An et al. (2024) analyzed panel data from 268 Chinese cities between 2011and 2020 to explore the interrelation of digital economy, green innovation, and sustainable development. Their study revealed that digital economy enhances both resource efficiency and environmental protection by promoting green innovation. The authors added that green innovation not only acts as a key intermediary but also moderates the relationship between the digital economy and sustainable development. Vărzaru et al. (2024) further support this connection, showing that technologies such as big data, AI, IoT, cloud computing, and robotics can significantly enhance sustainability by enabling process automation, efficient resource management, waste reduction, and increased productivity within the European Union.

In a more general manner, Singh and Jyoti (2023) examine the influence of digitalization on sustainable development by analyzing the following four key dimensions: Economic Development (ED), Social Development (SD), Environmental Sustainability (ES), and ICT. Their study evaluates these indicators across 34 countries, based on their rankings in Global Sustainable Development (GSD) and related contributing factors. The authors analyzed the relationships between the listed indicators by computing correlation coefficients and applied a log-linear regression model to estimate the effect of the explanatory variables on GSD and digitalization. Their findings suggest that achieving GSD is not possible without the presence of ED, SD, and ES.

Digitalization contributes positively to social, economic, and overall sustainable development, but it can adversely affect environmental sustainability. Digital technology allows GSD to increase, while GSD seems favorable to increasing digitalization. As Economic Development (ED), Social Development (SD), and GSD progress, the level of digitalization is likely to increase accordingly.

Furthermore, Castro and Lopes (2022) explored how digital governance relates to adjusted net savings, as a key measure of sustainable development. Employing a Logit model, they examined data from 103 countries over the period 2003 to 2018 to assess the impact of e-government initiatives on sustainable development.

Their findings indicate that the advancement of e-government plays a crucial role in promoting sustainable development, which includes economic, social, and environmental dimensions. Their study also suggested that e-government increases the likelihood of achieving sustainable development in developing and transition economies more than in developed countries.

For their parts, Oloyede et al. (2023) conducted research on defining and measuring the effect of the digital economy on national development, employing a systematic literature review guided by the PRISMA model. They also provided some recommendations to help policymakers address the digital divide and support the growth of the digital economy. Gomes et al. (2022) analyzed the extent to which the digital economy influences the development levels of OECD nations, categorizing them with respect to their degree of development. Their study showed that ICT contributes positively to economic growth in these countries, though the extent of this influence varies depending on the country’s level of development.

In a broader review of the field, Guandalini (2022) analyzed 153 scholarly works and found that while digitalization’s role in sustainable development is gaining traction among practitioners, academic research remains fragmented and slow to evolve. The study underscores the lack of standardized terminology and the disconnection between theory and practice, which challenges the coherent consolidation and practical application of current knowledge.

Hypothesis 1.

There is a positive correlation between digitalization and sustainable development.

2.2. Negative Relationship Between Corruption and Sustainable Development

While digitalization offers promising pathways toward achieving sustainable development, its transformative potential is not immune to contextual limitations. In particular, the presence of corruption can significantly diminish or distort the expected benefits of digital tools and platforms. In fact, corruption has long been recognized as a fundamental impediment to sustainable development. It erodes the effectiveness of institutions, distorts policy implementation, and perpetuates inequality, thereby directly and indirectly affecting the achievement of the Sustainable Development Goals. Several research studies have revealed inverse correlations between corruption and sustainable development. Ahmed and Anifowose (2024) carried out empirical work and demonstrated an inverse relationship with socio-economic development, as observed across the Asian, African, and Latin American and Caribbean (LAC) subpanels. Furthermore, corruption has a negative impact on environmental development in the Asian and African subpanels, whereas it exerts a positive effect in the context of the LAC subpanel. Kempe Ronald Hope (2024) found that corruption in African continues to be negatively associated with sustainable development objectives, significantly impeding the continent’s progress. Spyromitros and Panagiotidis (2022) demonstrated that corruption adversely affects economic growth in developing countries, though the relationship varies by region. While corruption generally exerts a negative influence on growth, evidence from Latin American countries indicates a potential positive association, highlighting regional heterogeneity in how corruption interacts with economic performance. This suggests that the impact of corruption on growth is context-dependent, potentially shaped by institutional quality, governance structures, or informal norms.

Using the threshold panel model developed by Seo and Shin (2016), Badur et al. (2024) analyzed data from 96 to 103 developed and developing countries spanning the period from 1996 to 2019. they found that the effect of corruption on sustainable development is regime-dependent and influenced by governance quality. In developed countries, corruption consistently hinders sustainable development. However, in developing countries, the relationship varies, as follows: when governance quality is low, higher corruption levels are associated with improved sustainable development outcomes, aligning with the “grease the wheels” hypothesis. Empirical evidence from a panel dataset covering 123 countries between 2000 and 2017 indicates a robust negative relationship between corruption and green growth (Tawiah et al., 2024). Utilizing pooled OLS as the primary estimator, supplemented by System-GMM and 2SLS-IV techniques to address country-specific heterogeneity and endogeneity, the study found that higher levels of corruption significantly hinder green-growth outcomes. Specifically, a 1% increase in corruption (as measured by control of corruption), relative to its standard deviation, is associated with a 15.47% decline in green growth, translating to approximately USD 0.912 less in green economic output per kilogram. These results are consistent across both developed and developing economies, underscoring the universal environmental cost of corruption and reinforcing the importance of anti-corruption efforts in advancing.

Hypothesis 2.

There is a negative correlation between corruption and sustainable development.

3. Data and Methodology

The methodology consists of two main parts. First, we develop a reference model to assess the impact of the digital economy on sustainable development, outlining the sample and key variables. Stationarity and heterogeneity of the variables are tested using statistical indicators and correlation matrices. Second, homogeneous and heterogeneous unit roots are tested following Levin et al. (2002) and Im et al. (2003), respectively. The long-term relationship is estimated using the fully modified technique, with stationarity checked via Pedroni’s (2000) tests. An error correction model is applied to assess the long-run fit, and the Generalized Method of Moments (GMM) is used to estimate the Human Development Index, incorporating lagged values and explanatory variables with Arellano and Bond’s (1991) one-step and two-step procedures.

Building on prior studies, Fhima et al. (2023), Sheikh et al. (2020), and Myovella et al. (2019), we establish the following empirical model:

$$\begin{gathered} Log{HDI}_{it}={\alpha }_0+{{\beta }_{1}LogTOP}_{it}+{{\beta }_{2}LogPOP}_{it}+{{\beta }_{3}LogFBCF}_{it}+{{\beta }_{4}LogPUBC}_{it}+{{\beta }_{5}LogMS}_{it}+{{\beta }_{6}LogINTU}_{it} \\ +{{\beta }_{7}LogFBS}_{it}+{{\beta }_{8}LogCPI}_{it}+{{\beta }_{9}LogCOCOR}_{it}+{\epsilon }_{it} \end{gathered}$$

(1)

where ${\alpha }_0$ is the individual specific effect; ${\beta }_{ij}$ ∀j = 1, … 11, i = 1, … 28 or =1, … 27 are the estimated coefficients; ${\epsilon }_{it}$ are the error terms; and «i» and «t» represent the individual and temporal dimensions, respectively.

The model variables are presented in

Table 1. Variables description.

Variable	Abbreviation	Definition	Source
Human Development Index	HDI	It is an annual composite index that assesses each country’s level of development based on indicators such as life expectancy, education, and per capita income.	World Bank’s Development Indicators
Trade openness of the economy	TOP	It refers to the total value of a country’s exports and imports of goods and services, expressed as a percentage of its Gross Domestic Product (GDP).	World Bank’s Development Indicators
Population growth rate	POP	Expressed as a percentage, the annual population growth rate is the exponential rate of change in the midyear population from year t − 1 to year t.	World Bank’s Development Indicators
Public consumption	PUBC	It includes all government current expenditures on the purchase of goods and services.	World Bank’s Development Indicators
Gross fixed capital formation	GFCF	Also known as gross domestic fixed investment, it encompasses expenditures on land improvements and machinery and equipment purchases, as well as the construction of infrastructure, such as roads, railways, schools, offices, hospitals, private residential buildings, and commercial and industrial structures.	World Bank’s Development Indicators
Mobile subscriptions	MS	It is the number of mobile cellular phone subscriptions per 100 inhabitants.	World Bank’s Development Indicators
Internet user	INTU	It represents the percentage of individuals who use the Internet.	World Bank’s Development Indicators
Fixed-broad band subscriptions	FBS	The fixed-broadband subscriptions per 100 inhabitants refer to high-speed fixed connections to the public Internet, including technologies such as 4G, 5G, and similar systems.	World Bank’s Development Indicators
Corruption Perception Index	CPI	It is the world’s most widely recognized corruption index, assessing the perceived level of public sector corruption in each country based on expert and businesspeople surveys.	Transparency International Organization
Control of corruption	CoCr	It reflects perceptions of how much public power is misused for private gain, encompassing both petty and grand corruption, as well as state capture by elites and private interests.	Transparency International Organization

.

Among various measurement indices, this study adopts the Human Development Index (HDI) as a proxy for sustainable development, serving as the sole endogenous (dependent) variable. The macroeconomic explanatory variables include Trade Openness (TOP), Population Growth Rate (PGR), Public Consumption (PUBC), and Gross Fixed Capital Formation (GFCF). The level of digital economy in each country is assessed through specific digital indicators, and we selected three indicators among five that were used by Zhang et al. (2022). The adopted indicators are as follows: Mobile Subscriptions (MS), Internet Users (INTU), and Fixed-Broadband Subscriptions (FBS). These digital indicators are largely considered in previous studies as key factors for the advancement of the digital economy. Finally, regarding the control variables, we introduced the following two corruption indicators: the Corruption Perception Index (CPI) and an element of the Control of Corruption (CoCr). In fact, we opted for these two indicators because they have an impact on sustainable development, thus mitigating the bias of the variables and improving the accuracy and precision of the estimated coefficients.

The regression model was used on 11 years’ worth of panel data from 2010 to 2020 for a sample of 28 developed countries and 27 developing countries.

Throughout the text, we abbreviate developing countries as LDCs and developed countries as DCs.

3.1. Descriptive Statistics

Table 2. Descriptive statistics of developed countries.

Variable	Mean	Standard Deviation	Variance	Skewness	Kurtosis
LHDI	10.2229	0.8410	0.7072	−1.2116	1.8345
LTOP	4.3266	0.5037	0.2537	0.3971	1.2267
LGPR	0.4876	1.3697	1.8763	3.0392	1.3601
LGFCF	3.1201	0.2089	0.0436	0.3837	2.7415
LPUBC	2.8966	0.7502	0.0436	0.3837	2.7415
LMS	3.4292	2.1376	4.56946	−1.9233	3.8449
LINTU	2.6052	2.6363	6.9502	−1.7442	2.8467
LFBS	4.1078	3.4179	11.6825	−0.9151	−0.2116
LCPI	1.6546	0.3412	0.1164	−0.4011	2.2005
LCoCr	2.4527	1.2078	1.4589	15.8014	2.2602

Source: processed data.

presents the descriptive statistics for the variables analyzed in developed countries. As shown, the arithmetic means are generally low across most variables, except for the Human Development Index, which has a high average value close to 10, indicating considerable variation in sustainable development levels. Moreover, the low standard deviations suggest limited variability, implying that each variable closely aligns with its mean, reflecting a strong linear fit.

The estimates reveal that the 28 developed countries exhibited variability across all variables. The Standard Deviation (SD) values for LHDI, LTOP, LGFCF, LPUBC, and LCPI are all below 1, indicating a strong linear fit. However, the skewness and kurtosis statistics suggest that most variables deviate from a normal distribution, as noted by Hair et al. (2022).

The descriptive statistics of developing countries are shown in

Table 3. Descriptive statistics of developing countries.

Variable	Mean	Standard Deviation	Variance	Skewness	Kurtosis
LHDI	6.1032	0.7314	0.5350	−0.1187	−0.5984
LTOP	4.1647	0.5174	0.2677	−0.2449	0.0665
LGPR	0.2643	0.6127	0.3754	−2.4145	16.3156
LGFCF	3.0619	0.3152	0.0993	−2.6403	15.4071
LPUBC	2.5438	0.3262	0.1064	−0.6499	2.1409
LMS	4.5627	3.3569	11.2694	−1.2513	0.7440
LINTU	0.6967	3.8034	14.4664	−1.0220	0.4271
LFBS	2.2663	4.1572	17.2826	−0.5573	−0.7997
LCPI	3.4946	0.2751	0.0756	−0.5385	0.4903
LCoCr	4.4839	1.0467	1.0956	5.3193	113.3010

Source: processed data.

. Note that the arithmetic means are positive and low for the distinct variables. Also, the standard deviations are very low for these variables; that is, the linear fit of each component of this database is very good compared to its average. In this same context, the precision qualities are very good for these variables, since the variances are very minimal. The skewness and kurtosis of the HDI and TOP variables are close to zero, which is considered a normal distribution.

When comparing the two studied samples, we clearly note that the respective means of the Human Development Index (HDI), Internet Users (INTU), and Fixed-Debt Subscriptions (FBS) in developed countries are higher than those of developing countries. However, we observe higher levels of corruption in developing countries. Therefore, we can carry out an initial classification of the two groups of countries according to their sustainable development indices and levels of digitalization.

3.2. Matrices’ Correlation Coefficients

The correlation matrix presented in

Table 4. Correlation matrix for developed countries.

	HDI	FBS	MS	CoCr	GFCF	TOP	GRP	INTU	CPI	PUBC
HDI	1.0000
FBS	0.1924	1.0000
MS	0.0888	−0.1626	1.0000
CoCr	0.4569	0.4265	−0.0917	1.0000
GFCF	0.1064	0.0286	−0.1970	0.1483	1.0000
TOP	0.6589	0.0615	0.1770	0.2350	−0.0238	1.0000
GRP	0.6656	0.0057	−0.0950	0.3651	0.0974	0.5049	1.0000
INTU	0.4634	0.6166	−0.1103	0.7736	0.1461	0.2744	0.2802	1.0000
CPI	0.3837	0.4206	−0.1289	0.9642	0.1496	0.1588	0.3019	0.7415	1.0000
PUC	0.2481	0.2296	0.1500	0.0859	−0.3269	−0.0756	−0.2400	0.1864	0.3445	1.0000

Source: processed data.

shows that the Human Development Index (HDI) relating to the sample of DP countries has a positive correlation with all variables. It is also noteworthy that there exists a strong correlation between the Human Development Index (HDI) and the Openness of the Economy (TOP), with a value of 0.6589, as well as the evolution of the population (GRP), with a value of 0.6656. We also note a non-significant negative correlation of Public Consumption (PUC) with the Fixed Capital Formation (GFCF), Trade Openness (TOP), and Population Growth Rate (GRP)variables. However, PUC is positively related to HDI, FBS, and CPI.

For the sample of developing countries,

Table 5. Correlation matrix for developing countries.

	HDI	FBS	MS	CoCr	GFCF	TOP	GRP	INTU	CPI	PUBC
HDI	1.0000
FBS	0.7698	1.0000
MS	0.4655	0.2983	1.0000
CoCr	0.6314	0.3947	0.2798	1.0000
GFCF	−0.2681	−0.0436	−0.4513	−0.2087	1.0000
TOP	0.2213	0.3057	0.0065	0.2667	−0.0266	1.0000
GRP	−0.6262	−0.7232	−0.3886	−0.3268	0.1339	−0.4442	1.0000
INTU	0.5378	0.6970	0.5679	0.2803	−0.3237	0.2428	−0.5245	1.0000
CPI	0.6242	0.4217	0.2945	0.9775	−0.1943	0.2685	−0.3423	0.3355	1.0000
PUC	0.1818	0.1232	0.0666	0.1768	−0.0597	−0.0327	−0.0574	0.1399	0.3478	1.0000

Source: processed data.

shows that the Human Development Index (HDI) is negatively correlated with Gross Fixed Capital Formation (GFCF) and Population Growth (GRP). Conversely, HDI exhibits positive correlations with Broadband Subscriptions (FBS), Mobile Subscriptions (MS), Control of Corruption (CoCr), Trade Openness (TOP), Internet Usage (INTU), and the Corruption Perception Index (CPI). Notably, the correlation between CPI and CoCr is particularly strong, with a coefficient of 0.9775.

4. Results and Discussions

Making correct decisions concerning the variables’ stationary properties is crucial for selecting an appropriate regression approach. Furthermore, determining the long-run nexus between dependent, independent, and control variables is an important step before moving on to our main estimation technique.

4.1. Unit Root Test

In this sub-section, we proceed to check the stationarity of the variables of our model in terms of the level and first difference. To reach this end, two panel unit root tests were conducted, as follows: Levin et al. (2002) test for the homogeneous case (noted LLC) and Im et al. (2003) (renowned IPS) test for the heterogeneous case.

Table 6. Levin et al. (2002) unit root test.

	Developed Countries						Developing Countries
Variable	Level			1st Difference			Level			1st Difference
	Rho-stat	t-Rho stat	ADF stat	Rho-stat	t-Rho stat	ADF stat	Rho-stat	t-Rho stat	ADF stat	Rho-stat	t-Rho stat	ADF stat
LHDI	2.1198	2.2005	2.4889	−42.301	2.5587	2.5402	3.3123	3.0854	5.1652	−39.3817	2.8599	2.1977
LTOP	0.8601	1.3950	1.7085	−39.6908	2.6102	2.0515	−1.0835	0.2977	0.3789	−45.1057	−0.2136	−0.7022
LGPR	−3.6122	−0.3008	−0.8245	−43.3920	0.9786	1.6145	0.5247	0.4051	1.1429	−38.5112	0.3811	0.7668
LGFCF	−5.0023	−1.4872	−1.6217	−50.0142	2.1720	2.4022	−4.4256	−1.0649	−0.6948	−39.7920	0.6987	0.1465
LPUBC	−2.8679	0.3112	0.4995	−40.1052	2.2985	2.1263	−2.5987	−0.6950	−0.3955	−40.8601	0.8007	0.1423
LMS	−1.1936	−5.4917	−0.4155	−18.7793	2.9583	2.6223	0.1996	−3.0555	0.7703	−18.8892	−2.3012	−2.0954
LINTU	−1.7115	−5.3962	1.7962	−19.9022	1.1289	1.4127	0.5012	−2.1003	−0.0119	−30.1542	−7.2354	−6.2225
LFBS	1.6908	0.5102	1.6687	−23.7992	−0.1230	−0.3123	2.4887	1.6944	4.0154	−27.2887	−3.3132	−4.0613
LCPI	−1.8662	−0.1455	−0.0203	−40.5887	2.2265	1.8954	−1.1068	0.5619	0.5430	−46.9857	0.4005	0.3982
LCOCOR	−1.1502	0.5362	0.6111	−49.7592	−0.6755	0.3529	−0.7132	0.4011	0.6362	−50.3720	−1.2988	−1.6102

Source: processed data.

presents the results of Levin et al. (2002) non-stationarity test for the different components, distinguishing between developed and developing countries.

As shown in Table 6, the model variables exhibit homogeneous unit roots according to Levin et al. (2002) test. This is evidenced by test statistics that exceed the critical value of −1.64 at the 5% significance level. However, after applying the first difference, all variables become stationary, as indicated by the bolded test values in Table 6, which fall below the −1.64 threshold. Similarly,

Table 7. Im et al. (2003) unit root test.

	Developed Countries		Developing Countries
	Level	1st Difference	Level	1st Difference
LHDI	2.7322	−3.2215	4.1783	−3.1250
LTOP	0.4516	−2.77102	−1.09598	−2.87045
LGPR	1.0623	−2.55809	−1.5874	−2.7651
LGFCF	3.7451	−3.1526	2.7085	−2.3425
LPUBC	2.7052	−2.5082	2.6520	−2.0280
LMS	−0.3781	−3.0821	1.1025	−2.7958
LINTU	2.3121	−1.8069	−1.02411	−7.6919
LFBS	2.0685	−2.1934	3.2975	−5.4378
LCPI	−0.1325	−2.5063	−1.1425	−2.6958
LCOCOR	1.3115	−2.3936	0.8090	−6.12263

Source: processed data.

reveals that the variables display heterogeneous unit roots at that level, based on Im et al. (2003) test, where the test statistics are greater than the 5% critical value of −1.64. Upon first differencing, the IPS test’s statistics fall below this threshold, confirming stationarity. Thus, all variables in the dataset—across both developed and developing countries—are integrated to an order of one, I (1).

Once the data-variables have the same level of integration, the cointegration of the panels should be taken into account to ensure that the studied variables have a long-term relationship. For this purpose, the Pedroni (2000) cointegration test was employed.

4.2. Co-Integration on Panel Data

Pedroni (2000) has proposed various tests to address the null hypothesis of no co-integration for both homogeneous and heterogeneous panels. This author proposes an extension to the case where cointegration relationships involve more than two variables. These tests take into account heterogeneity through parameters that differentiate individuals. The heterogeneity can be located at the level of the cointegration relationship or at the level of short-run dynamics. The results are presented in

Table 8. Pedroni residual cointegration test.

	Common AR Coef. (Within-Dimension)				Individual AR Coef. (Between-Dimensions)
	Rho-stat	v-stat	pp-stat	Adf-stat	Rho-stat	pp-stat	Adf-stat
Residusit developed countries	−3.1764	−2.4530	−3.1334	−3.0976	−2.1254	−1.8976	−1.9987
Residusit developing countries	−2.7853	−2.432	−3.4521	−4.4031	−1.9842	−1.9932	−2.0986

Source: processed data.

below.

Table 9. Long-run coefficients by FM procedure.

	Developed Countries		Developing Countries
	Coefficient	t-Statistic	Coefficient	t-Statistic
LTOP	0.11	7.42 ***	−0.03	−3.81 ***
LGPR	−0.03	−2.19 **	−0.10	−11.23 ***
LGFCF	0.31	20.87 ***	0.20	11.77 ***
LPUBC	−0.03	−3.98 ***	−0.01	−1.69 *
LMS	0.05	2.01 **	−0.02	−0.15
LINTU	0.15	3.22 ***	0.04	2.33 **
LFBS	0.49	22.75 ***	0.02	16.88 ***
LCPI	−0.09	−3.43 ***	0.03	4.86 ***
LCOCOR	−0.15	−3.18 ***	−0.01	−3.28 ***

Source: processed data. (***) (**) (*) Significant at the 1%, 5% and 10% levels, respectively.

below presents the seven Pedroni (2000) tests of stationarity at the residual level or the target of each relationship that links the sustainable development index as a function of macroeconomic variables, digital economy indicators, and institutional variables for the two groups of developed and developing countries. All of the calculated statistics are lower than the critical value of the centered normal distribution that is reduced to the 5% risk threshold. So, we accept the existence of a long-term relationship and estimate it using the Fully Modified Least Squares (FM-OLS) technique, which was initially proposed by Phillips and Hansen (1990) and then extended by Phillips (1995). The estimation results are listed in Table 9.

The estimation of the long-term relationships that link the sustainable development index to the explanatory variables by the modified-least-squares technique with respect to trade openness is significantly negligible. This implies that the contribution of trade openness to this index is very low and even marginal on the socio-economic prosperity of the developed countries. It is also true that the impact of population growth on this sustainable development is very low despite being negative and significant.

On the other hand, the elasticity of this index with respect to gross fixed capital formation is high. Nevertheless, the impact of public spending on this sustainable development is very modest, but remains significant. Mobile subscriptions are less involved in this development, but broadband subscriptions have a strong influence on this sustainable development.

Hence, the development of the digital economy is ensured by the massive use of the Internet and broadband subscriptions. Thus, it is clear that the institutional variables have a negative contribution to this sustainable development.

Estimates of the long-run relationship between social and human development in developing countries are also significant except for public spending. Unlike developed countries, we observe that external openness causes a slight decline in sustainable development of these countries. Similar to developed countries, the population growth rate causes a decline in the sustainable development index of developing countries although the size of this decrease is much greater in these countries than in developed countries.

It is also worth noting that the sustainable development index in developing countries is very flexible with regard to gross fixed capital formation, and the contribution of public expenditure is very marginal. The digital economy indicators have very marginal contributions to socio-economic development, as the elasticity of this index relative to these indicators tends toward zero and are significant. Furthermore, while our hypothesis assumes a clear negative link between corruption and sustainable development, the empirical results reveal only a marginal effect. Corruption control and corruption index have very marginal effects on the socio-economic development of these twenty-seven developing countries. This does not necessarily contradict the theory, but rather suggests that in many developing economies, corruption may be so embedded within institutional structures that its developmental impact is more subtle, normalized, or indirect. Furthermore, widely used corruption indices primarily capture perception-based data, which may not fully reflect the diverse ways corruption manifests or affects specific components of sustainability. These findings highlight the importance of considering contextual factors and measurement approaches when analyzing the role of corruption in development processes. Hence, the human development of developed countries is ensured by this digital economy, but the latter has no contribution to the sustainable development of developing countries.

We now turn to the analysis of the linear fit of each long-run relationship estimated by the fully modified technique within an Error Correction Model (ECM).

This ECM combines the deterministic equilibrium (where variables are stationary by first difference) and long-run equilibrium (where variables are stationary by linear combination), provided that the unit root tests reveal that the residuals or target are stationary at the level. The estimation results of the ECMs are presented in

Table 10. ECM estimation.

	Developed Countries		Developing Countries
	Coefficient	Significance	Coefficient	Significance
Constant	0.1333 ***	0.0000	0.0796 ***	0.0000
$\Delta$LTOP	0.0632 ***	0.0001	−0.0411 **	0.0152
$\Delta$LGPR	2.3460	0.8865	0.0137	0.2041
$\Delta$LGFCF	0.1301 ***	0.0000	0.1054 ***	0.0000
$\Delta$LPUBC	−1.3087	0.3215	0.0198 **	0.0377
$\Delta$LMS	7.8497 **	0.0229	0.0017	0.6113
$\Delta$LINTU	1.5844	0.6001	−0.0013	0.5559
$\Delta$LFBS	4.8975 **	0.0212	0.0053 **	0.0469
$\Delta$LCPI	−0.0112	0.2875	0.0207	0.1985
$\Delta$LCoCoR	−9.0876	0.1211	0.00109	0.6321
Residusit−1	−0.0245	0.0000 ***	−0.0059	0.0019 ***

Source: processed data. (***) (**) Significant at the 1% and 5% levels, respectively.

below.

Estimating the deterministic and long-run equilibrium using the modified-least-squares method for the sample of developed countries yields non-significant positive coefficients in the short run for the variables GRP, PUBC, INTU, CPI, and CoCr. However, we observe a very high repercussion of the two numerical indicators, FBS and MS, on the HDI which is reflected by significantly positive coefficients at a threshold of 5%. As for the effect of the TOP variable, it is significantly positive but remains weak. For developing countries, the coefficients of the digital variables are significantly zero. Therefore, the digital economy does not influence the HDI of these countries in the short term

The residuals of each lagged long-run relationship have a negative and significant coefficient. Hence, there is an adjustment mechanism that brings each sustainable development target back to a partially stable situation in the long-term.

This mechanism allows for rectifying the disequilibrium of these relationships over the long-term in developed and developing countries, which are around 2.45% and 0.59%, respectively.

4.3. System GMM Estimation

The estimation of dynamic models of panel data provides a set of techniques, most used of which are notably the methods of Anderson and Hsiao (1982) and Arellano and Bond (1991). Although with the first method we achieve convergent estimator, this technique does not exploit all of the conditions of the moments, and it does not take into account the structure in terms of the error. Therefore, in this study, we chose Arellano and Bond’s (1991) method, which is more efficient. See Hendayanti and Nurhidayati (2023) for a more detailed presentation of this model.

The Hausman Test for endogeneity checks whether certain explanatory variables in a regression model are endogenous. For the null hypothesis (H₀), in each case, the variable is exogenous (i.e., not correlated with the error term). Since all p-values < 0.05, we reject H₀ for all variables at the 5% significance level. This confirms that each variable is endogenous, and OLS is not appropriate. To address this endogeneity, you should use the GMM or 2SLS by utilizing valid instrumental variables for consistent estimation.

Furthermore, the results of the Arellano-Bond test are presented in

Table 11. Diagnostic test.

Hausman TEST for Endogeneity		p-Value (Prob > x)
Null Hypothesis	Test Statistic (χ²)
TOP is exogenous	9.65	0.002
GPR is exogenous	5.64	0.011
GFCF is exogenous	5.19	0.023
PUBC is exogenous	8.27	0.025
MS is exogenous	7.62	0.012
INTU is exogenous	10.21	0.001
FBS is exogenous	6.09	0.014
CPI is exogenous	7.19	0.010
CoCr is exogenous	7.64	0.006
Testing autocorrelation
AR (1) autocorrelation test	−3.38 (0.001)
AR (2) autocorrelation test	−0.76 (0.446)
Testing Heteroscedasticity
Null hypothesis (H0): the variance of the residuals is constant (homoscedasticity).
Breusch-Pagan test	Prob > χ = 0.018

, illustrating that the AR (2) test resulted in a value for the prob = 0.446 > 0.05, failing to reject H₀ (no second-order serial correlation in the residuals of the differenced equation) and that autocorrelation does not happen. In addition, Breusch–Pagan test indicates that the null hypothesis of homoscedasticity is rejected because the p-value (0.018) is less than 0.05, and this confirms that there is heteroscedasticity in the model. All of these results are good sign of the validity and consistency of the GMM model.

The results of the Human Development Index (HDI) estimation lagged by a single period, along with macroeconomic variables, digital economy indicators, and institutional variables in the developed and developing countries, according to the “one-step” and “two-step” procedures of Arellano and Bond (1991), as shown in

Table 12. Dynamic panel data estimation by the GMM method.

Variable	Developed Countries		Developing Countries
	One-Step	Two-Step	One-Step	Two-Step
	Coefficient	Coefficient	Coefficient	Coefficient
Constant	−7.8473	2.87015 **	−0.3485	−0.0179
LHDIit−1	0.0247	1.0758 ***	−0.8977 *	0.8997 ***
LTOPit	3.9487	0.1898 ***	−0.0348	0.0053
LGPRit	−0.0029	−0.0205	−0.0441 **	0.0498 ***
LGFCFit	−3.8896	0.3122 ***	0.1055 **	−0.2965 ***
LPUBCit	4.2765	−0.0234	−0.1066	−0.0105 **
LMSit	−0.0728	0.2234 **	0.0116	0.0712
LINTUit	0.0848	1.4357 ***	−0.0144	−0.3081 ***
LFBSit	−0.15587	−0.5603 ***	−0.0012	0.4121 ***
LCPIit−1	0.0240	0.1643	0.0113	−0.2024
LCoCoRit	0.2115	−0.0043	0.2371 **	0.0689 ***
“Over-identifying restrictions” test statistics
Sargan	1.624493 × 109	61,012.016245	14.5581	289.0876
Significance	0.8077	0.1502	0.6979	0.1599

(*) Significant at a 10% risk; (**) significant at 5%; and (***) significant at 1%.

below.

From Table 12, we deduce that the estimation of the dynamic model using the two-step technique of Arellano and Bond (1991) leads to more consistent results than the one-step technique (Roodman, 2009). The two-step Generalized Method of Moments (GMM) estimator, developed by Arellano and Bond (1991), is well-suited for dynamic panel data with a short time dimension and potential endogeneity. The inclusion of a lagged dependent variable introduces bias that conventional estimators, like fixed or random effects, cannot address. Additionally, variables such as internet use, capital formation, and corruption are likely endogenous. The Arellano–Bond approach overcomes these issues by using internal instruments and eliminating unobserved country-specific effects through first differencing. We apply the two-step estimator with robust standard errors, which enhances efficiency and accounts for heteroskedasticity. Diagnostic tests support the model’s validity; the AR(2) test confirms no second-order autocorrelation, and the non-significant Sargan test indicates that the instruments are valid. Thus, the two-step GMM results serve as the basis for our analysis.

The first observation concerns the coefficient of the lagged variable in this dynamic model, which takes a positive and significant sign for both developed and developing countries. Of course, the level of development achieved yesterday can only improve today’s situation.

In the context of developing countries, the obtained results show that broadband subscription, internet users, control of corruption, gross fixed capital formation, and population change have significant coefficients but with different signs.

The results for Fixed-Broadband Subscriptions (FBS), Anti-Corruption Rate (CoCr), and Population Growth Rate (GPR) show positive impacts on sustainable development, with coefficients of 0.4121, 0.0689 and 0.0498, respectively. In contrast, both the GFCF and INTU variables show a negative effect on sustainable development, with coefficients of −0.2965 and −0.3081, respectively. These variables are significant in our model at a 1% risk level. These results, while contrary to the predominant literature, highlight the unique developmental challenges faced by many developing economies. In such contexts, increased internet use may not contribute to sustainable development if digital infrastructure is poorly distributed or if usage patterns do not support productivity and environmental awareness. Similarly, fixed capital formation may fail to deliver developmental benefits if investments are misallocated, inefficiently managed, or undermined by weak institutions and governance structures. This suggests that technological inputs and capital accumulation alone are insufficient; they must be complemented by effective policy frameworks, institutional quality, and capacity-building measures to translate into meaningful and sustainable outcomes.

The coefficients of Mobile Subscriptions (MS) and Trade Openness of the economy (TOP) variables were positive, but not significant. It is noteworthy that the PCI variable had a negative coefficient (−0.2024) and was not significant. Although Public Consumption (PUBC) significantly affected the Human Development Index, its effect remained negative and very weak.

Concerning developed countries, our findings indicate that Mobile Subscriptions (MS) and Internet Users (INTU) present positive and significant coefficients at the 1% and 5% levels, respectively, while the coefficient of Broadband Subscription (FBS) is negative. Furthermore, the INTU variable has a positive and significant effect on the HDI at a 1% threshold. In fact, a 1% increase in the number of internet users contributes to a 1.4357% increase in sustainable development. Similarly, the contribution of trade openness to sustainable development is positive, with a coefficient of 0.1898.

We also observe a notable difference in the signs for gross fixed capital formation in our estimated model for the two groups of countries. Indeed, although the GFCF coefficient is significant at the 1% level, it is positive (0.3122) for developed countries and negative (−0.2965) for developing countries. Moreover, the coefficients of the institutional variables differ between the two groups of countries. The coefficient of Control of Corruption (CoCr) is positive and significant at the 1% level only for developing countries. However, the estimated coefficients of the Corruption Perspective Index have different signs, as follows: −0.2024 for developing countries and 0.1643 for developed countries. These signs remain non-significant in both cases.

As already mentioned, the impact of the Internet on sustainable development is significantly positive. Thus, the deployment of fixed Internet, beyond being a simple communication tool, as it appears to be in developing countries, plays an important role as basic digital infrastructure affecting almost all sectors of developed economies. This may illustrate the differences in how people use technologies. Regarding the population growth rate variable in developing countries, its estimated coefficient shows a positive contribution to sustainable development. For its part, the corruption control variable positively affects sustainable development in developing countries. However, the estimation result is surprising for developed countries, as its coefficient is negative and non-significant. This could be explained on the one hand, by the abuse of power through corruption practices, which can hinder economic and social development, and, on the other hand, by the non-application of sanctions which renders distinct measures to combat corruption ineffective. When it comes to trade openness, it seems to play a key role in developed countries. This shows that economies that are relatively open to the outside world are developing faster than those that are relatively less liberalized. Thus, thanks to this openness, countries can acquire the needed equipment for digital development.

5. Conclusions, Policy Implications and Suggestions for Further Studies

In recent years, there has been a growing body of research examining the role of digitalization in a country’s sustainable development. In this context, the current paper sought to explore the influence of digital economy on sustainable development by conducting a panel regression analysis consisting of 28 developed and 27 developing countries between 2010 and 2020.

Drawing on an evaluation of various studies that examine both macroeconomic and non-economic factors, we referred to the Human Development Index (HDI) of each country as our endogenous variable, representing sustainable development. For the independent variables, we chose four key macroeconomic indicators—Trade Openness (TOP), Population Growth Rate (PGR), Gross Fixed Capital Formation (GFCF), and Public Consumption (PUBC). Additionally, we incorporated three elements related to the digital economy, as follows: Mobile Subscriptions (MS), Internet Users (INTU) and Fixed-Broadband Subscriptions (FBS). Finally, we used the following two control variables: the Corruption Perception Index (CPI) and Control Corruption (CoCr).

In fact, the originality and novelty of our work stems from the fact that we employed a range of econometric techniques, including panel data co-integration analysis and the Generalized Method of Moments (GMM), to empirically examine the impact of digital economy on sustainable development. To our knowledge, there are still no studies that analyze the link between digital economy and sustainable development using a variety of econometric methods as comprehensive as the one adopted in the current study. The objective was to properly and accurately ascertain and specify the significance of this ambiguous relationship. Furthermore, this study is significant for its use of a comparative approach to assess the extent to which digitalization contributes to sustainable development, taking into account the disparities in Human Development Index between developed and developing countries.

In addition, we complement our empirical analysis on a more in-depth study based on a dynamic panel model that relates the Human Development Index to its lagged value, macroeconomic variables, digital economy indicators, and institutional variables. The findings from the two-step system GMM estimation indicated that broadband subscriptions, population change, and corruption control all have a significant and positive influence on sustainable development in developing countries.

On the other hand, internet usage and the accumulation of fixed capital negatively impact sustainable development. (ii) Mobile subscriptions, internet users, and trade openness are interesting variables in our study. They result in positive and significant contributions to the Human Development Index in the developed countries model. (iii) The variable representing corruption control exhibits a statistically significant and positive effect, exclusively in the context of developing countries.

Regarding the use of internet networks in underdeveloped countries, our empirical findings can be explained due to the less developed internet network infrastructure. This certainly has implications for policymakers, who should also take appropriate measures to leverage the benefits of digitalization to promote sustainable economic growth. This can be achieved by investing in human capital development and enacting effective, forward-looking governmental policies.

Furthermore, this research opens up promising avenues to take into account the effect of global crises, such as the COVID-19 pandemic, on digitalization and sustainable development. We can consider that the digital economy is the engine of socio-economic development—an assertion that no longer needs to be demonstrated. One may wonde, however, how digital technology, digitization, and the Internet can be considered effective solutions for the regulation of cryptocurrencies and for ensuring the stability of the global ecosystem. Hence, future research should integrate variables contributing to innovation, competitiveness, and growth. Similarly, incorporating other variables related to cybercriminal activity and cryptocurrencies, such as Bitcoin, may yield more significant and comprehensive results, enriching our understanding of the digital economy’s broader implications.