Full article: Human development and economic growth: who benefits the most?

I. Introduction

To promote national development, the United Nations Development Programme (UNDP) advocates sustainable growth through its 17 Sustainable Development Goals (SDGs), which serve as a global framework for addressing long-term challenges. Central to this vision is human development, recognized as essential for achieving sustainable progress. When individuals are healthy, educated, secure, and enjoy a decent quality of life, they contribute more effectively to economic and social advancement. Defined by the UNDP (Citation1990) as the expansion of people’s choices and improvement in quality of life, human development encompasses three key dimensions: a long and healthy life, access to knowledge, and a decent standard of living.

To measure this comprehensively, the UNDP introduced the Human Development Index (HDI) in 1990. Unlike conventional economic indicators such as GNP per capita, the HDI integrates health, education, and income to assess human development more holistically. Without adequate investment in human capital and infrastructure, income growth alone may not translate into improved well-being (UNDP Citation1990). Figure 1 illustrates HDI trends across regions from 2015 to 2022. The global average HDI increased from 0.724 to 0.739, with the East Asia and Pacific region experiencing the largest growth (5.21%), followed by South Asia (4.05%), Sub-Saharan Africa (3%), Europe and Central Asia (2.55%), the Arab States (1.88%), and Latin America and the Caribbean (0.65%).

When examining the HDI by income groups – high, middle and lowFootnote¹—between 2015 and 2022, as illustrated in Figure 2, high-income countries have the highest average HDI, with a mean value of 0.878. This is followed by middle-income and low-income countries, with average HDI values of 0.757 and 0.579 respectively. The HDI for all three income groups exhibits an upward trend, increasing by 1.86% in high-income countries, 0.75% in middle-income countries, and 2.28% in low-income countries over the period.

In addition to the HDI, the UNDP introduced the Inequality-adjusted Human Development Index (IHDI) and the Planetary Pressures-adjusted Human Development Index (PHDI),Footnote² as shown in Figures 3 and 4. Both indices show an upward trend from 2015 to 2022, with the IHDI rising by 3.86% (from 0.568 to 0.581) and the PHDI increasing by 1.74% (from 0.649 to 0.657).

When disaggregated by income group, high-income countries recorded the highest average IHDI (0.800), followed by middle- and low-income countries at 0.621 and 0.411, respectively. A similar pattern was observed for the PHDI, with values of 0.754, 0.705, and 0.557, respectively. All income groups experienced growth in both indices: the IHDI increased by 2.54% in high-income countries, 2.48% in middle-income countries, and 6.24% in low-income countries, while the PHDI rose by 2.03%, 1.01%, and 2.25%, respectively.

The rise in human development can have significant implications for economic growth, as it reflects improved well-being, greater access to healthcare and education, and expanded income-generating opportunities. These factors enhance living standards and resource access, while improved health and education increase labour productivity and economic participation. Therefore, human development plays a critical role in fostering economic growth (Ntogwa Citation2012; Rahman, Raja, and Ryan Citation2020). Endogenous growth theory further supports the notion that investments in human capital – such as education, skill development and research and development – enhance knowledge and living standards, ultimately leading to higher productivity and economic expansion (Rahman, Raja, and Ryan Citation2020).

Despite its overall benefits, uneven distribution of human development can lead to diminishing returns for economic growth. When concentrated among high-income individuals, specific sectors, or those with better infrastructure access, large segments – particularly low-income groups – may be excluded from contributing to productive activities. In high-income countries, where human development is already advanced, additional improvements in human capital may yield limited economic gains. Moreover, in developing or middle-income countries, rising human capital can lead to brain drain, as skilled individuals migrate abroad, potentially undermining domestic economic growth (Ntogwa Citation2012).

Given these dynamics, previous research has explored this relationship and found that the impact of human development economic growth is evident; however, most studies focus on specific regions or country groups, such as developing nations, ASEAN countries, African nations, or selected developed and developing countries (Elistia, Syahzuni, and Elistia Citation2018; Gulcemal Citation2020; Hoa, Liem, and Phuoc Citation2016; Rahman, Raja, and Ryan Citation2020; Selvia and Idris Citation2021; Sopradit and Saosaovaphak Citation2024; Teker and Güner Citation2016). Other studies have examined individual countries (Iskandar Citation2017; Ma’wa and Cahyadi Citation2023; Taqi et al. Citation2021), or specific provinces within countries Dinar, Hasan, and Arisah (Citation2021); Lestari, Marhaeni, and Yasa (Citation2021); Iskandar (Citation2017). Moreover, previous research on developed and developing nations has primarily focused on the direct relationship between human development and economic growth, without considering other potential factors influencing economic expansion (Elistia, Syahzuni, and Elistia Citation2018; Ntogwa Citation2012; Thienprasert Citation2017).

To address the limitations of prior research, this study investigates the impact of human development on economic growth using a comprehensive dataset of 188 countries,Footnote³ offering a globally representative analysis that extends beyond single-country or region-specific studies. By incorporating a diverse set of countries, it examines how this relationship varies across income levels and geographic regions, recognizing that differences in economic structures, social conditions, and geographic characteristics can influence outcomes. Moreover, the study employs not only the standard Human Development Index (HDI) but also its two adjusted forms – the Inequality-adjusted Human Development Index (IHDI) and the Planetary Pressures-adjusted Human Development Index (PHDI) – to provide a more nuanced assessment of human development’s role in economic performance. Furthermore, the study controls for a range of economic and social factors, ensuring a more robust and policy-relevant analysis than previous research.

II. Literature review

Human development is linked to economic growth in theoretical terms. According to the neoclassical growth model (Mankiw, Romer, and Weil Citation1992; Solow Citation1956), aggregate output depends on physical capital, labour and technological progress, with savings rates, population growth and technology treated as exogenous factors. Mankiw, Romer, and Weil (Citation1992) further highlights the role of human capital and population growth in influencing economic growth. Endogenous growth theory, developed by Lucas (Citation1988) and Romer (Citation1990, Citation1994), emphasizes that human capital, labour skills, physical capital and technological advancement are key drivers of growth. Barro and Sala-I-Martin (Citation2004) also argue that increases in physical capital, technology, labour input and human capital contribute to higher growth. Additionally, macroeconomic and development theories identify education, health and infrastructure as social factors that enhance access to resources and improve productivity. Economic factors – such as investment, consumption, government spending, trade openness, employment and financial development – also play a crucial role in raising output and promoting economic growth (Mankiw Citation2023).

In addition, human development reflects better health, education and a higher standard of living. An increase in human development can enhance labour productivity and improve access to infrastructure, resources and income, thereby promoting economic growth. However, if these improvements are concentrated in specific groups, such as high-income countries, other groups may continue to face limited access to essential resources and economic opportunities. This inequality can hinder their participation in productive activities, ultimately slowing overall growth. Moreover, in countries where human development is already high, such as developed or high-income nations, further increases may lead to diminishing returns, thereby reducing the positive impact on economic growth (Elistia, Syahzuni, and Elistia Citation2018; Ntogwa Citation2012; Rahman, Raja, and Ryan Citation2020).

Empirical studies have examined the relationship between human development and economic growth, often focusing on specific regions or country groups. Teker and Güner (Citation2016) found a positive correlation between HDI and GDP across 12 developed and developing countries (2000–2013), highlighting the roles of education, health, and employment. Similarly, Rahman, Raja, and Ryan (Citation2020), analysing panel data from 50 countries (2000–2014), confirmed the positive impact of human development, alongside trade openness and physical capital investment. Focusing on developing countries, Gulcemal (Citation2020) reported a significant effect of human development on growth across 16 nations (1990–2018), with additional contributions from government revenue, employment, and low inflation. Hoa, Liem, and Phuoc (Citation2016) found that HDI, employment, and physical capital positively influenced growth in 30 countries (1999–2014). In the ASEAN-10, Elistia, Syahzuni, and Elistia (Citation2018) identified a positive relationship between HDI and GDP per capita (2010–2016), though their study did not account for other influencing factors. Likewise, Ntogwa (Citation2012) showed a positive correlation between human development and growth, without controlling for confounding variables.

Several studies examine specific dimensions of human development rather than its comprehensive impact. For instance, Ramos, Surinach, and Artís (Citation2012) found that education – measured by labour force education levels and years of schooling – positively influenced economic growth. Sarwar et al. (Citation2020) confirmed these findings in emerging markets, linking higher education enrolment to increased GDP per capita. Health-focused studies, such as Selvia and Idris (Citation2021) and Bloom et al. (Citation1998, Citation2003), identified a positive relationship between life expectancy and economic growth across multiple countries. Shome and Tondon (Citation2010) found that life expectancy and literacy positively correlated with GDP per capita in ASEAN-5 countries. Sopradit and Saosaovaphak (Citation2024) showed that education, life expectancy, and employment positively impacted economic growth, whereas unemployment had a negative effect. Broader studies by Ranis, Stewart, and Ramirez (Citation2000.), and Sultana, Dey, and Tareque (Citation2022) confirmed the significant role of education and health in driving growth. Additionally, Požega, Crnkovic, and Sučić (Citation2010) highlighted the importance of workforce skills and reduced corruption in promoting GDP per capita across countries. Country-specific studies also support these broader findings (Iskandar Citation2017; Lestari, Marhaeni, and Yasa Citation2021; Taqi et al. Citation2021).

Overall, the literature suggests that human development plays a significant role in promoting economic growth, with education and health identified as the most influential dimensions. However, many studies focus on specific components of development, rather than adopting a comprehensive approach or limited to specific regions or country groups. Furthermore, previous studies often emphasize the direct relationship between human development and economic growth, without adequately accounting for other economic and social factors that may influence such growth.

III. Methodology and data

To investigate the effect of human development on economic growth, the study employed the following model:

(1) $y_{\mathit{i},\mathit{t}}={\alpha }_i+{\beta }_1{y}_{\mathit{i},\mathit{t}-1}+{\beta }_2\mathit{HD}{I}_{\mathit{i},\mathit{t}}+{\beta }_3\mathit{in}{v}_{\mathit{i},\mathit{t}}+{\beta }_4\mathit{co}{n}_{\mathit{i},\mathit{t}}+{\beta }_5\mathit{go}{v}_{\mathit{i},\mathit{t}}+{\beta }_6\mathit{trad}{e}_{\mathit{i},\mathit{t}}+{\beta }_7\mathit{u}{n}_{\mathit{i},\mathit{t}}+{\mathit{\beta }}_8\mathit{f}{d}_{\mathit{i},\mathit{t}}+{\mathit{\beta }}_9\mathit{tec}{h}_{\mathit{i},\mathit{t}}+{\beta }_{10}\mathit{depen}{d}_{\mathit{i},\mathit{t}}+{\beta }_{11}\mathit{rul}{e}_{\mathit{i},\mathit{t}}+{\beta }_{12}\mathit{in}{f}_{\mathit{i},\mathit{t}}+{\beta }_{13}\mathit{covi}{d}_t+{\epsilon }_{\mathit{i},\mathit{t}}$(1)

where y_i,t is the economic growth rate of country i in year t.

${\alpha }_i$ is the individual country’s characteristics that might influence the economic growth of each country.

HDI_i,t represents human development of country i in year t. In addition, the UNDP has enhanced the Human Development Index to account for inequality in human development across populations within countries, as well as for environmental factors that may influence it. IHDI_i,t, the Inequality-adjusted Human Development Index and, PHDI_i,t, the Planetary Pressures-adjusted Human Development Index.

Theoretical frameworks and empirical studies suggest that human development can exert both positive and negative effects on economic growth. On the positive side, improvements in education and health enhance labour productivity, increase output, and support economic expansion. Higher quality of life and earning potential enable greater access to resources, goods, and services, thereby stimulating investment, consumption, and overall growth.

Conversely, if human development is unevenly distributed – benefiting primarily high-income groups or specific sectors – low-income populations may remain excluded from productive opportunities, limiting broader economic benefits. In developed countries, where human development is already high, further investments in human capital may yield diminishing returns. Additionally, in developing or middle-income countries, rising human capital may contribute to brain drain, as skilled workers migrate abroad, potentially constraining domestic growth.

In the model estimation, each of these indices is analysed separately in individual equations to capture the distinct effects of human development on economic growth reflected through each type of index.

The study also includes a set of control variables related to economic, social and demographic factors that may influence economic growth. Details of these are given below:

inv_i,t refers to physical investment in an economy. According to economic growth theories and previous research, suggests that higher investment increases production, and ultimately higher economic growth.

con_i,t represents consumption expenditure, which is a fundamental driver of GDP and economic growth. Increases in household and government consumption boost aggregate demand, stimulating economic activity. Government spending on infrastructure, defence, and public services further enhances production capacity and contributes to sustained economic growth.

trade_i,t refers to trade openness. Openness to international trade enhances market access, fosters production for export, and increases income and productivity, ultimately contributes to higher economic growth.

un_i,t is unemployment. A rise in unemployment reduces individuals’ income and spending capacity, limits access to credit, and consequently diminishes consumption, investment and production. These conditions negatively affect economic growth.

fd_i,t denotes financial development. When financial institutions are more capable of lending, suggesting sector expansion and improved access to credit for individuals, businesses and households. This promotes investment, production and economic growth. However, excessive credit may pose financial stability risks, such as lending to subprime borrowers or investing in risky assets, potentially leading to liquidity issues, loan defaults and financial crises, which may adversely affect economic growth.

tech_i,t refers to technological development, which fosters investment, production and overall economic growth.

depend_i,t represents the dependency ratio, where a higher dependency ratio indicates a greater burden on working-age individuals and households, as well as increased governmental obligations to support dependents. This reduces productive capacity, thereby constraining economic growth.

rule_i,t refers to institutional quality, reflecting good governance, justice and respect for individual rights. Better institutional quality leads to a better protection of rights, peace, political stability and good governance, all of which foster economic development and growth.

inf_i,t denotes inflation, which can have varying impacts on economic growth. Generally, low and stable inflation supports growth by encouraging spending and investment. However, high and volatile inflation can hinder growth by creating uncertainty, reducing investment, and distorting markets.

covid_i,t is a COVID-19 pandemic dummy variable. The pandemic leads to negative effects on economic growth due to restrictions on travel, exports and imports, and production. These disruptions led to reduced domestic investment and consumption.

ε_i,t is the error term.

Moreover, since the impact of human development on economic growth may differ depending on a country’s income level, EquationEquation (1)(1) $y_{\mathit{i},\mathit{t}}={\alpha }_i+{\beta }_1{y}_{\mathit{i},\mathit{t}-1}+{\beta }_2\mathit{HD}{I}_{\mathit{i},\mathit{t}}+{\beta }_3\mathit{in}{v}_{\mathit{i},\mathit{t}}+{\beta }_4\mathit{co}{n}_{\mathit{i},\mathit{t}}+{\beta }_5\mathit{go}{v}_{\mathit{i},\mathit{t}}+{\beta }_6\mathit{trad}{e}_{\mathit{i},\mathit{t}}+{\beta }_7\mathit{u}{n}_{\mathit{i},\mathit{t}}+{\mathit{\beta }}_8\mathit{f}{d}_{\mathit{i},\mathit{t}}+{\mathit{\beta }}_9\mathit{tec}{h}_{\mathit{i},\mathit{t}}+{\beta }_{10}\mathit{depen}{d}_{\mathit{i},\mathit{t}}+{\beta }_{11}\mathit{rul}{e}_{\mathit{i},\mathit{t}}+{\beta }_{12}\mathit{in}{f}_{\mathit{i},\mathit{t}}+{\beta }_{13}\mathit{covi}{d}_t+{\epsilon }_{\mathit{i},\mathit{t}}$(1) was re-estimated to account for variations among low-, middle-, and high-income country groups. Specifically, the model in EquationEquation (1)(1) $y_{\mathit{i},\mathit{t}}={\alpha }_i+{\beta }_1{y}_{\mathit{i},\mathit{t}-1}+{\beta }_2\mathit{HD}{I}_{\mathit{i},\mathit{t}}+{\beta }_3\mathit{in}{v}_{\mathit{i},\mathit{t}}+{\beta }_4\mathit{co}{n}_{\mathit{i},\mathit{t}}+{\beta }_5\mathit{go}{v}_{\mathit{i},\mathit{t}}+{\beta }_6\mathit{trad}{e}_{\mathit{i},\mathit{t}}+{\beta }_7\mathit{u}{n}_{\mathit{i},\mathit{t}}+{\mathit{\beta }}_8\mathit{f}{d}_{\mathit{i},\mathit{t}}+{\mathit{\beta }}_9\mathit{tec}{h}_{\mathit{i},\mathit{t}}+{\beta }_{10}\mathit{depen}{d}_{\mathit{i},\mathit{t}}+{\beta }_{11}\mathit{rul}{e}_{\mathit{i},\mathit{t}}+{\beta }_{12}\mathit{in}{f}_{\mathit{i},\mathit{t}}+{\beta }_{13}\mathit{covi}{d}_t+{\epsilon }_{\mathit{i},\mathit{t}}$(1) was re-estimated by incorporating dummy variables for income groups and interaction terms between each type of Human Development Index and the income group dummy variables, as follows:

(2) $y_{\mathit{i},\mathit{t}}={\alpha }_i+{\beta }_1{y}_{\mathit{i},\mathit{t}-1}+{\beta }_2\mathit{HD}{I}_{\mathit{i},\mathit{t}}+{\beta }_3\mathit{hig}{h}_i+{\beta }_4\mathit{middl}{e}_i+{\beta }_5(\mathit{hig}{h}_i\ast \mathit{HD}{I}_{\mathit{i},\mathit{t}})+{\beta }_6(\mathit{middl}{e}_i\ast \mathit{HD}{I}_{\mathit{i},\mathit{t}})+{\beta }_7\mathit{in}{v}_{\mathit{i},\mathit{t}}+{\beta }_8\mathit{co}{n}_{\mathit{i},\mathit{t}}+{\beta }_9\mathit{trad}{e}_{\mathit{i},\mathit{t}}+{\beta }_{10}\mathit{u}{n}_{\mathit{i},\mathit{t}}+{\mathit{\beta }}_{11}\mathit{f}{d}_{\mathit{i},\mathit{t}}+{\mathit{\beta }}_{12}\mathit{tec}{h}_{\mathit{i},\mathit{t}}+{\beta }_{13}\mathit{depen}{d}_{\mathit{i},\mathit{t}}+{\beta }_{14}\mathit{rul}{e}_{\mathit{i},\mathit{t}}+{\beta }_{15}\mathit{in}{f}_{\mathit{i},\mathit{t}}+{\beta }_{16}\mathit{covi}{d}_t+{\epsilon }_{\mathit{i},\mathit{t}}$(2)

where high_i,t and middle_i,t are dummy variables signifying income level of the country.

The HDI data, together with those of the adjustment indices (IHDI and PHDI), of 188 countries over the period 2015–2022 were collected from the UNDP.Footnote⁴ For economic growth and other country characteristics, the data were collected from the World Development Indicator (WDI) (World Bank, Citation1990). A description of the data and their sources of data are given in Table 1.Footnote⁵ Summary statistics of all the variables used in the study are presented in Table 2.Footnote⁶ Additional summary statistics by income level and by region are provided in Tables 3 and 4, respectively.

The estimation of EquationEquations (1)(1) $y_{\mathit{i},\mathit{t}}={\alpha }_i+{\beta }_1{y}_{\mathit{i},\mathit{t}-1}+{\beta }_2\mathit{HD}{I}_{\mathit{i},\mathit{t}}+{\beta }_3\mathit{in}{v}_{\mathit{i},\mathit{t}}+{\beta }_4\mathit{co}{n}_{\mathit{i},\mathit{t}}+{\beta }_5\mathit{go}{v}_{\mathit{i},\mathit{t}}+{\beta }_6\mathit{trad}{e}_{\mathit{i},\mathit{t}}+{\beta }_7\mathit{u}{n}_{\mathit{i},\mathit{t}}+{\mathit{\beta }}_8\mathit{f}{d}_{\mathit{i},\mathit{t}}+{\mathit{\beta }}_9\mathit{tec}{h}_{\mathit{i},\mathit{t}}+{\beta }_{10}\mathit{depen}{d}_{\mathit{i},\mathit{t}}+{\beta }_{11}\mathit{rul}{e}_{\mathit{i},\mathit{t}}+{\beta }_{12}\mathit{in}{f}_{\mathit{i},\mathit{t}}+{\beta }_{13}\mathit{covi}{d}_t+{\epsilon }_{\mathit{i},\mathit{t}}$(1) and (Equation2(2) $y_{\mathit{i},\mathit{t}}={\alpha }_i+{\beta }_1{y}_{\mathit{i},\mathit{t}-1}+{\beta }_2\mathit{HD}{I}_{\mathit{i},\mathit{t}}+{\beta }_3\mathit{hig}{h}_i+{\beta }_4\mathit{middl}{e}_i+{\beta }_5(\mathit{hig}{h}_i\ast \mathit{HD}{I}_{\mathit{i},\mathit{t}})+{\beta }_6(\mathit{middl}{e}_i\ast \mathit{HD}{I}_{\mathit{i},\mathit{t}})+{\beta }_7\mathit{in}{v}_{\mathit{i},\mathit{t}}+{\beta }_8\mathit{co}{n}_{\mathit{i},\mathit{t}}+{\beta }_9\mathit{trad}{e}_{\mathit{i},\mathit{t}}+{\beta }_{10}\mathit{u}{n}_{\mathit{i},\mathit{t}}+{\mathit{\beta }}_{11}\mathit{f}{d}_{\mathit{i},\mathit{t}}+{\mathit{\beta }}_{12}\mathit{tec}{h}_{\mathit{i},\mathit{t}}+{\beta }_{13}\mathit{depen}{d}_{\mathit{i},\mathit{t}}+{\beta }_{14}\mathit{rul}{e}_{\mathit{i},\mathit{t}}+{\beta }_{15}\mathit{in}{f}_{\mathit{i},\mathit{t}}+{\beta }_{16}\mathit{covi}{d}_t+{\epsilon }_{\mathit{i},\mathit{t}}$(2) ) employs the System Generalized Method of Moments (System GMM) approach, developed by Blundell and Bond (Citation1998) as an extension of the difference GMM estimator by Arellano and Bond (Citation1991). This method is particularly suitable for dynamic panel data models with potential endogeneity and unobserved individual effects. The System GMM approach addresses unobserved individual effects and endogeneity by including the lagged dependent variable, first-differencing the model, and employing instrumental variables. Unlike Difference GMM, which uses instruments only in the differenced equation, System GMM incorporates instruments in both the level and differenced equations. This enhances estimation efficiency, mitigates potential sample bias, and ensures more reliable inference in dynamic panel data models.

IV. Results

The results of the estimation of EquationEquation (1)(1) $y_{\mathit{i},\mathit{t}}={\alpha }_i+{\beta }_1{y}_{\mathit{i},\mathit{t}-1}+{\beta }_2\mathit{HD}{I}_{\mathit{i},\mathit{t}}+{\beta }_3\mathit{in}{v}_{\mathit{i},\mathit{t}}+{\beta }_4\mathit{co}{n}_{\mathit{i},\mathit{t}}+{\beta }_5\mathit{go}{v}_{\mathit{i},\mathit{t}}+{\beta }_6\mathit{trad}{e}_{\mathit{i},\mathit{t}}+{\beta }_7\mathit{u}{n}_{\mathit{i},\mathit{t}}+{\mathit{\beta }}_8\mathit{f}{d}_{\mathit{i},\mathit{t}}+{\mathit{\beta }}_9\mathit{tec}{h}_{\mathit{i},\mathit{t}}+{\beta }_{10}\mathit{depen}{d}_{\mathit{i},\mathit{t}}+{\beta }_{11}\mathit{rul}{e}_{\mathit{i},\mathit{t}}+{\beta }_{12}\mathit{in}{f}_{\mathit{i},\mathit{t}}+{\beta }_{13}\mathit{covi}{d}_t+{\epsilon }_{\mathit{i},\mathit{t}}$(1) examining the impact of human development on economic growth across countries worldwide are shown in Table 5.Footnote⁷ Column (1) presents the results for the impact of human development on economic growth using the HDI. Column (2) shows the results using the Inequality-adjusted HDI, while column (3) displays those based on the Planetary Pressures-adjusted HDI. The statistical test results in Table 5 indicate that the Hansen test does not reject the null hypothesis of instrument validity, suggesting that the model is suitably specified with valid instruments.

As shown in Table 5, none of the three Human Development Indices (HDI, IHDI, and PHDI) exhibit a statistically significant effect on economic growth, suggesting that aggregate human development does not universally influence growth across countries. However, this result reflects a global average and may obscure differences by income level, where the impact of human development may vary.

Among the control variables, the lagged value of economic growth shows a statistically significant negative effect across all models, indicating that high growth in the previous year is associated with slower growth in the current year. This may reflect diminishing returns, where elevated growth levels are difficult to sustain. In contrast, gross capital formation as a share of GDP (inv) – a proxy for physical investment – has a significant positive effect on economic growth. This finding supports classical, neoclassical, and endogenous growth theories, and is consistent with prior studies (e.g. Bloom, Canning, and Sevilla Citation2003; Hoa, Liem, and Phuoc Citation2016; Rahman, Raja, and Ryan Citation2020), affirming the role of capital accumulation in promoting economic growth.

The results for trade openness indicate a statistically significant positive effect on economic growth. An increase in trade openness is associated with increases in economic growth across models (1)–(3). These findings align with theoretical expectations that trade openness expands market access, enhances export production, and raises national income, thereby promoting productivity and growth. This is consistent with prior studies by Rahman, Raja, and Ryan (Citation2020). Furthermore, the unemployment rate exhibits a statistically significant negative impact on economic growth across models (1)-(3). This supports the notion that higher unemployment reduces income, consumption, investment, and overall economic activity. These findings are in line with those of Hoa, Liem, and Phuoc (Citation2016), Teker and Güner (Citation2016), and Selvia and Idris (Citation2021). Inflation is found to have a negative impact on economic growth across models (1)-(3), suggesting that higher inflation is associated with lower economic growth, consistent with prior empirical studies (Barro Citation1995; Bruno and Easterly Citation1998; Fischer Citation1993), which suggest that inflation, particularly when high or volatile, undermines growth through its adverse effects on investment, savings, and resource allocation. Additionally, the COVID-19 pandemic variable exhibits a statistically significant negative impact on economic growth. The pandemic disrupted economic activity globally – through reduced production, consumption, and trade – leading to slower growth across countries.

To examine the heterogeneous effects of human development on economic growth across income groups, dummy variables for high-income (high = 1) and middle-income countries (middle = 1) were introduced, with low-income countries serving as the reference group. These dummies were interacted with each HDI to capture differential impacts by income level. Country classifications were based on the World Bank’s GNI per capita criteria. Table 6 presents the interaction results: Column (1) uses the HDI, Column (2) the IHDI, and Column (3) the PHDI, allowing for an assessment of how the relationship between human development and economic growth varies by income group. The statistical test results in Table 6 indicate that the Hansen test does not reject the null hypothesis of instrument validity, suggesting that the model is suitably specified with valid instruments.

As can be seen in Table 6Footnote⁸, the impact of human development on economic growth differs by income level. Specifically, column (1) shows that human development itself does not have a statistically significant effect on economic growth. However, the interaction term HDI × middle does have a statistically significant positive effect. Taken together, the HDI and HDI × middle variables indicate a significant joint effect. This suggests that a 1% increase in the Human Development Index results in an increase in the economic growth rate of middle-income countries by approximately 2.653%.Footnote⁹ This positive effect of human development on economic growth in middle-income countries is consistent with expectations. Improvements in human development often lead to enhanced education, skills development, better health outcomes, and improved quality of life forCitation2000. the population. As a result, labour productivity rises, leading to higher national output, ultimately fostering economic growth. Notably, this significant effect occurs only in middle-income countries. The findings are in line with previous studies (Gulcemal Citation2020; Rahman, Raja, and Ryan Citation2020; Sultana, Dey, and Tareque Citation2022; Teker and Güner Citation2016).

When considering the impact of the IHDI, the results in column (2) indicate that the IHDI variable itself does not have a statistically significant effect on economic growth. However, the interaction terms IHDI × middle and IHDI × high exhibit significantly positive effects on such growth. The joint significance of the IHDI variable and its interaction terms with middle- and high-income groups further confirms their overall significant impact. These findings imply that a 1% increase in the Inequality-adjusted Human Development Index would raise the economic growth rate by approximately 1.367% in middle-income countries and 1.214% in high-income countries.Footnote¹⁰ The positive impact observed from inequality-adjusted human development suggests that eliminating inequality in the distribution of health, education and income dimensions across countries can enhance economic growth in both middle- and high-income groups. These results support the expectation that adjusting for inequality in human development (as captured by the IHDI) leads to more equitable distribution of development benefits, thereby enhancing economic productivity and growth. However, the IHDI remains statistically insignificant for low-income countries, suggesting that improvements in human development alone may not suffice to drive growth without addressing underlying structural barriers. These findings are in line with prior studies – such as Teker and Güner (Citation2016), Rahman, Raja, and Ryan (Citation2020), Lestari, Marhaeni, and Yasa (Citation2021), Gulcemal (Citation2020), Sultana, Dey, and Tareque (Citation2022)—which highlight the stronger impact of human development on economic growth in middle-income and developing economies.

When considering the effects of the PHDI, Column (3) demonstrates that the PHDI variable itself does not have a statistically significant impact on economic growth. However, the interaction term PHDI × middle has a positive and statistically significant effect on growth, with a coefficient of 2.008, significant at the 1% level. The variables PHDI and PHDI × middle are jointly significant, indicating that a 1% increase in the environmentally adjusted human development index leads to a 1.72% increase in the economic growth rate of middle-income countries.Footnote¹¹ The positive impact of human development – when adjusted for environmental pressures – is found only among middle-income countries, with no significant effects observed in high- or low-income ones.

This suggests that the impact of human development, whether measured by the standard HDI, the Inequality-adjusted HDI (IHDI), or the Planetary pressures-adjusted HDI (PHDI), is statistically significant in middle-income countries. However, when inequality is accounted for (as in the IHDI), human development is also found to significantly affect economic growth in high-income countries. Nevertheless, across all three HDI variants, no significant effect is observed for low-income nations. This lack of significance may be attributed to the relatively low levels of human development found in these countries. According to Figures 2–4 and Table 3, low-income countries have the lowest average values of HDI, IHDI, and PHDI when compared to both high- and middle-income ones, and below the global average. This situation reflects low levels of development in areas such as health and nutrition, education and standard of living. Low-income countries also face limitations in access to basic infrastructure, public health services and education. These conditions may explain why improvements in human development in these countries are not yet sufficient to significantly impact economic growth. Moreover, even when adjustments are made to account for inequality and environmental impacts, human development levels in low-income countries remain low. This indicates that these countries continue to suffer from inequality in health, education and quality of life indicators, along with serious environmental challenges – including pollution and excessive use of natural resources for final goods and services production. Such conditions may negatively affect environmental sustainability and hinder the ability of human development to contribute meaningfully to economic growth in low-income countries, even after adjusting for these factors.

For middle- and high-income countries, the average values of the human development indices are relatively high. This situation reflects the considerable emphasis placed on human development in these income groups, especially among middle-income countries, which are striving to overcome the middle-income trap and transition into high-income status. As such, increases in all three forms of human development indices in this group have statistically significant impacts on economic growth. However, in high-income countries, issues related to inequality in human development may still persist, particularly in areas such as education, health and overall quality of life, which may be concentrated among specific population groups. Therefore, when controlling for inequality using the IHDI it becomes apparent that human development has a significant impact on economic growth in high-income countries. This suggests that inequality-adjusted human development plays a crucial role in driving economic growth in this group. On the other hand, general human development (HDI) and environmentally adjusted human development (PHDI) do not significantly affect economic growth in these countries. This may be because they already exhibit the highest levels of human development, including the environmental dimension, when compared to other income groups. Therefore, any further increase in HDI or PHDI yields diminishing returns in terms of their contribution to economic growth, due to the already high baseline levels.

Moreover, the results show that the dummy variables for high- and middle-income groups have significant negative effects on economic growth. Specifically, the middle-income dummy variable has statistically significant negative coefficients of −202.302, −88.280 and −138.688 at the 1% significance level across Columns (1) to (3) respectively. This suggests that, ceteris paribus, middle-income countries tend to experience lower economic growth rates compared to low-income ones. Similarly, the high-income dummy variable also shows a statistically significant negative effect on economic growth, with a coefficient of −127.893 and −115.123 at the 10% and 1% significance level, as shown in Column (1) to (2) respectively. This implies that high-income countries also exhibit lower growth rates than low-income ones, particularly when inequality-adjusted human development is used as the main explanatory variable. This could reflect the fact that high levels of development are often associated with slower, more stable growth trajectories, in contrast to the more rapid catch-up growth seen in lower-income countries.

With regard to the control variables included in the model, the results show that the lagged economic growth rate continues to have a statistically significant negative effect across all three specifications. Specifically, a high level of economic growth in the preceding year is associated with a significant decline in the current year’s growth rate. This suggests the presence of mean reversion in global economic growth patterns, whereby periods of high growth are often followed by a slowdown. In terms of inflation, the results indicate a negative and statistically significant impact on economic growth, consistent with the findings reported in Table 5. Furthermore, the study finds that the COVID-19 pandemic had a significantly negative effect on economic growth across all model specifications. This result underscores the severe and widespread economic disruptions caused by the pandemic during the study period.

V. Conclusion

Human development is one of the core objectives of the United Nations, as it plays a crucial role in influencing both the economy and economic growth. Improvements in such development within a country reflect a higher quality of life for its citizens, greater access to healthcare and education services, and increased income opportunities. These improvements contribute to better living standards and public health, which in turn lead to enhanced productivity, greater engagement in economic activities, and ultimately higher levels of economic growth. However, if human development is concentrated within only specific groups – such as high-income individuals, certain economic sectors, or those with privileged access to infrastructure – its benefits may not be broadly shared. In such cases, economic growth may slow down or increase at a diminishing rate. Previous research has often focused on specific country groups or regions. Comprehensive studies that examine the relationship between human development and economic growth across all countries, and income groups remain limited.

Therefore, this study investigated the impact of human development on economic growth across countries with different income levels, using three key indices: the Human Development Index (HDI); the Inequality-adjusted Human Development Index (IHDI); and the Planetary pressures – adjusted Human Development Index (PHDI). Utilizing panel data from a broad set of countries and applying the system GMM estimation technique, the results indicate that human development positively influences economic growth in middle-income countries. When inequality is accounted for (IHDI), the positive relationship extends to high-income countries, highlighting the role of equitable development in promoting growth. In contrast, no significant effect is observed in low-income countries – regardless of the index used – likely due to their persistently low human development levels, as reflected in their average HDI, IHDI and PHDI values compared to global averages. Regional analysis reveals broadly consistent patterns with the global results, though some differences emerge. Europe, North America, and Latin America – regions dominated by high- and middle-income economies – consistently show a strong and significant link between human development and growth across all three indices. In these regions, higher baseline development levels are likely to enhance the capacity of human development to generate economic gains. By contrast, regions with larger share of low-income countries such as South Asia tend to show insignificant effects, suggesting that lower development levels may limit the extent to which human development contributes to growth. These findings indicate that while human development generally supports economic growth, its effectiveness can vary by regional income structure and stages of development.

Therefore, to enhance the role of human development in driving economic growth, policy strategies should be tailored to the income and regional context. Middle-income countries should prioritize equitable access to healthcare, education and quality of life improvements to stimulate growth. High-income countries, despite having high overall human development, need to reduce disparity in access to health, education and living standards to sustain growth. Policymakers in these countries should focus not only on improving human development, but also on addressing inequality to ensure that the benefits of development are widely shared. For low-income countries, foundational investments in basic services, infrastructure and institutional capacity are critical for future economic growth.

The study also reveals that economic growth across countries is significantly influenced by a range of factors. Higher investment and trade openness are associated with increased growth, while higher unemployment and inflation decrease it – highlighting the need for investment in physical capital, trade promotion and labour upskilling, particularly in low- and middle-income countries. However, such growth strategies must also address inequality in access to resources and the environmental costs of industrialization. The COVID-19 pandemic further underscores the importance of resilient healthcare systems. Future economic stability depends on investment in healthcare infrastructure, equitable access to services and effective disease prevention to mitigate the impact of public health crises on economic performance.

While this study provides valuable insights using comprehensive cross-country data, one limitation lies in its reliance on national-level aggregates, which may not fully capture intra-country disparities in human development and economic performance. Given the observed variation across income groups and regions, future research could benefit from subnational data – where available – to further explore localized dynamics and provide more nuanced policy recommendations.

Statements and declarations

This work (KUREC-SSR67/178) was reviewed by the Kasetsart University Research Ethics Committee and received exemption (COE NO. COE67/142).