Issues related to the relationship between population and economic growth remain widely debated. Population can be viewed either as a driver or as a constraint on economic growth. In high-income countries, low population growth is considered a challenge for sustaining economic growth (Peterson 2017). Conversely, in low-income countries, high population growth can hinder economic growth. Brida et al. (2024) reveal that there are distinct differences in the relationship between population growth and economic growth across mature, transition, and young economies. Mature economies, mostly OECD countries, are characterized by low population growth and high economic growth. In contrast, young economies, such as those in Central Africa, are characterized by high population growth but low economic growth. Meanwhile, transition economies experience initially high population growth during the early stages of development, followed by declining population growth while maintaining above-average economic performance.

Empirical findings on the relationship between population and economic growth have not resolved the longstanding controversy between the two. There is no theoretical consensus on the scope and channels through which population and economic growth influence each other (Cayssials et al. 2024). However, addressing this controversy requires a more nuanced understanding that population issues should not be viewed solely in aggregate terms. Population dynamics can be further examined through age structure. The age structure of the population reflects the composition of working-age and non-working-age groups, including both young and elderly dependents. Wang and Lee (2019) show that age structure has a nonlinear effect on economic growth. Age structure is commonly represented by the age dependency ratio, which consists of child and old-age dependency ratios. The child dependency ratio exhibits a U-shaped effect, while the old-age dependency ratio shows an inverted U-shaped effect on economic growth. Meanwhile, Bidisha et al. (2020) provide empirical evidence that age structure, as measured by the age dependency ratio, has a long-run effect on economic growth measured by GDP per capita. Specifically, the age dependency ratio has a significant negative impact on GDP per capita growth. Therefore, an increase in the working-age population relative to the non-working-age population, reflected in a decline in the age dependency ratio, is associated with higher GDP per capita growth.

The issue of economic growth related to the population’s age structure has been widely studied. However, studies examining economic growth in relation to population age structure generally measure growth in terms of GDP growth or GDP per capita. There are still relatively limited studies that interpret economic growth in terms of productivity growth. GDP growth primarily reflects the expansion of aggregate national income, while GDP per capita growth captures changes in average income from the perspective of income recipients, namely the population. In contrast, output growth inherent in the concept of aggregate labor productivity is more appropriate when associated with the population’s age structure. Moreover, labor productivity, as a proxy for technological progress, has a dominant positive impact on economic growth, as shown in a cross-regional study by Badriah and Arintoko (2024).

The relationship between population age structure and productivity growth has generally been examined with an emphasis on the importance of the working-age population for productivity. Population age structure is commonly represented by the age dependency ratio. A higher age dependency ratio implies a lower proportion of the productive-age population relative to the non-working population, which includes both young and elderly groups. Fu et al. (2020) provide empirical evidence that changes in age structure, reflected in increasing total dependency and child dependency ratios, have a negative impact on labor productivity across provinces in China. Differences in population dividends and levels of urbanization contribute to heterogeneous development patterns across provinces.

ASEAN is one of the most interesting regional areas for examining the relationship between population factors and productivity growth. The ASEAN region contributes the third-largest share of the global population, after India and China. However, there are substantial variations in population characteristics across member countries. Trong et al. (2024) emphasize that demographic variables play an important role in driving per capita output growth.

Labor productivity is closely associated with a country’s competitiveness in supporting economic development (Bangun 2017). Labor productivity in the five selected ASEAN countries, as illustrated in Figure 1, shows an overall upward trend across all countries. Nevertheless, significant differences and variations in productivity levels persist among them. The five selected countries are emerging economies characterized by relatively advanced economic structures and high levels of investment aimed at achieving rapid growth. These countries also represent the five most populous ASEAN members, excluding Myanmar. Considerable variation in labor productivity is observed, with Malaysia exhibiting the highest productivity level and Vietnam the lowest.

Along with increasing labor productivity, the age dependency burden tends to decline, as shown in Figure 2. Variations in the age dependency burden reflect differences in population age structures among the five countries. The Philippines exhibits the highest dependency burden, while Thailand has the lowest. Accordingly, the share of the working-age (productive-age) population in the Philippines is relatively smaller compared to that in Thailand.

A key question arises: does the age structure of the population affect labor productivity? More specifically, does the burden of age dependency dampen productivity when foreign direct investment (FDI) and private sector credit increase? FDI and credit are key factors that can stimulate employment and enhance productivity in growth-oriented economies. Accordingly, this study examines whether the age dependency burden moderates the effects of FDI and private sector credit on productivity when both variables exert a positive influence on productivity.

Empirical evidence from several previous studies (including Tőkés 2019; Asada 2020; Yunus and Abdullah 2022) shows that FDI has a positive impact on productivity. Meanwhile, other studies (including Manaresi and Pierri 2024; Pham and Phan 2024) provide evidence that credit also positively affects productivity. Banks remain the primary source of credit for the private sector, as they continue to rely heavily on interest income and maintain high loan-to-deposit ratios (Arintoko et al. 2024). Alongside increases in FDI and private sector credit, ASEAN countries have also experienced rising government debt and persistent budget deficits. Similarly, financial development in the region has been accompanied by growth in broad money.

According to the production function developed by Solow, output per worker is a function of capital per worker (Mankiw 2016). In Solow’s growth theory, labor alone cannot drive growth in output per worker; rather, it must interact with capital. Capital accumulation occurs through investment, which may originate from foreign direct investment and private sector credit. Previous studies on the determinants of labor productivity typically treat the working-age population as an independent factor influencing productivity. Accordingly, the age dependency ratio is often included as an independent variable affecting productivity, as shown in studies by Huang et al. (2019) and Fu et al. (2020).

If the effect of the age dependency ratio is not purely independent, it may operate through interactions with FDI and credit in influencing productivity. This study is therefore motivated to develop a model of productivity determinants that incorporates moderating effects. The age dependency ratio is specified as a moderating variable, given its close association with employment dynamics arising from investment driven by FDI and private sector credit. An increase in the age dependency ratio implies a decline in the share of the working-age population, which may constrain labor productivity even in the presence of rising FDI and private sector credit, alongside increasing government debt and broad money.

The moderating role of the age dependency ratio is examined using the Arellano–Bond GMM framework, with labor productivity as the dependent variable. The explanatory variables include foreign direct investment, private sector credit, government debt, and broad money. In this specification, the age dependency ratio moderates the effects of FDI and credit on aggregate labor productivity.

Economic growth cannot be separated from a country’s population structure and population growth. Population structure refers to the composition of the working-age population in terms of age and skills, which has a significant impact on labor productivity. Consequently, changes in population structure affect not only employment levels but also productivity across different population groups. The working-age population typically includes individuals aged 15 to 64 years. This age group constitutes the labor force, which consists of individuals who are either employed or actively seeking employment. The composition of the working-age population, including its age distribution and skill levels, influences aggregate employment, labor productivity, and overall economic growth.

Huang et al. (2019) provide empirical evidence that workforce aging has a complex relationship with economic growth. While the old-age dependency ratio has a negative effect on economic growth, human capital plays a crucial role in total factor productivity (TFP). Thus, both labor force characteristics and population age structure significantly affect productivity through TFP. In contrast, empirical findings by Hernæs et al. (2023) offer different evidence, showing that an increase in the proportion of workers aged 63–67 has a small positive effect on labor productivity in the short run.

The study by Maestas et al. (2023) finds that population aging, reflected in an increasing share of the population aged 60 and above, leads to a decline in GDP per capita growth. This decline arises partly from slower employment growth and more substantially from reduced labor productivity growth. Similarly, Trong et al. (2024) show that old-age dependency reduces GDP per capita growth in ASEAN countries. Meanwhile, productive young workers remain a key resource for economic development and GDP per capita growth. Overall, demographic variables play a significant role in driving GDP per capita growth.

Choudhary et al. (2016) provide empirical evidence that the population’s age composition affects labor productivity growth. A higher age dependency ratio is associated with lower labor productivity. Moreover, age dependency can modify the impact of other factors on productivity. Both child and old-age dependency ratios exert negative effects on labor productivity, although with differing magnitudes.

Population aging shapes age composition and age dependency ratios through an increasing share of elderly individuals relative to the child population. Alarussi and Yen (2023) show that population aging has a negative impact on labor productivity. Within the age dependency ratio framework, the proportions of child and old-age dependents jointly determine the total dependency ratio. An increase in the total age dependency ratio implies a reduction in the share of the working-age population. In addition to high fertility episodes, population aging contributes to rising age dependency. As population aging reduces the working-age population while simultaneously increasing the elderly population, economic growth may decline (Lee and Shin 2019), partly through reduced productivity. Overall, empirical evidence consistently indicates that the age dependency ratio negatively affects productivity. For example, Park and Shin (2023) find that both old-age and young-age dependency ratios have adverse effects on labor productivity.

The age dependency ratio is closely related to foreign direct investment (FDI) in influencing productivity, as this variable directly affects employment. Nguyen et al. (2024) show that increasing FDI has a positive impact on employment creation. Through FDI, foreign firms tend to have greater capacity to generate employment than domestic firms, creating spillover effects from foreign to domestic enterprises (Dao et al. 2023). Saucedo et al. (2020) further find that rising FDI inflows positively affect both low- and high-skilled employment in the manufacturing sector.

With respect to productivity, Dandi et al. (2024) report that FDI positively affects labor productivity in ASEAN countries. Similarly, Tőkés (2019) demonstrates that, at the industry level, FDI has a positive impact on labor productivity. Using micro-level data, Karentina (2019) finds that FDI enhances labor productivity in the long run. According to Vinh (2019), this positive effect of FDI on labor productivity primarily arises from technology transfer.

In this study, the age dependency ratio, which is closely associated with FDI through its effect on employment, is treated as a moderating variable in the relationship between FDI and productivity. Consistent with the Solow growth model, population factors are assumed to interact with capital in determining output per worker. Capital accumulation occurs through investment, one of the main sources of which is FDI inflows. Through this interaction, an increase in the age dependency ratio is expected to weaken the positive effect of FDI on productivity growth.

In addition to FDI, the interaction between the age dependency ratio and private sector credit also influences productivity growth. Regarding employment creation, an increase in bank credit directly affects employment (Ginting and Widyawati 2022). Acosta and Cortés (2022) and Gutierrez et al. (2023) show that positive credit shocks significantly enhance employment creation. Bank credit can stimulate private sector investment, thereby increasing employment opportunities for the working-age population. The age dependency ratio, when acting as a moderator of private sector credit, is expected to reduce the positive effect of credit on productivity.

Beyond the moderating role of the age dependency ratio on FDI and credit, other factors influencing labor productivity growth include government debt and financial sector development, proxied by the money supply. These control variables can affect labor productivity growth when the age dependency ratio moderates the impact of FDI and credit. According to Afonso and Jalles (2013), the government debt ratio can support the growth of total factor productivity, including labor productivity. Higher government debt allows for increased public investment, which can expand employment and enhance labor productivity. Similarly, Haris and Mohammad (2015) find that government debt can lead to more effectively allocated government investment, contributing to productivity growth in both the manufacturing and service sectors.

Money supply is positively associated with labor productivity growth. A tight monetary policy stance, which reduces the money supply, can suppress both productivity and employment (Fornaro and Wolf 2023). Such tightening negatively affects investment and productivity growth. Conversely, a loose monetary policy that expands the money supply can stimulate investment and enhance productivity. Jolaiya (2024) further emphasizes that increases in money supply and private sector credit positively affect domestic investment. Higher domestic investment, in turn, provides opportunities to boost productivity. Similarly, Dang et al. (2020) note that monetary policy can encourage private investment by increasing the money supply. Empirical evidence also indicates that, in addition to public investment, private investment strongly and positively impacts output growth (Turan et al. 2021). Therefore, money supply positively affects private investment and has the potential to enhance labor productivity.

Based on the previous literature, the following hypotheses are proposed:

H1a: FDI has a positive effect on labor productivity

H1b: The age dependency ratio moderates the positive effect of FDI on labor productivity.

H2a: Credit has a positive effect on labor productivity

H2b: The age dependency ratio moderates the positive effect of credit on labor productivity.

H3: Government debt has a positive effect on labor productivity

H4: Broad money has a positive effect on labor productivity

The estimation results indicate that the positive effect of FDI on labor productivity growth diminishes as the age dependency ratio increases. The rise in investment due to FDI inflows has a limited impact on labor productivity when the share of the working-age population (ages 15–64) declines relative to the non-working population (ages below 15 and above 64). These findings extend the empirical literature on the adverse effects of the age dependency burden on labor productivity, as noted by Choudhary et al. (2016) and Park and Shin (2023). This study provides new insights by showing that the age dependency ratio affects labor productivity primarily through moderation, rather than a direct effect. Labor contributes to production largely through its interaction with capital growth rather than independently.

A similar pattern is observed for private sector credit: its positive effect on productivity decreases as the age dependency ratio rises. In other words, a smaller share of the working-age population reduces the productivity-enhancing effect of private-sector credit. However, the empirical results in this study do not provide sufficient evidence to confirm a positive effect of credit on productivity. Excessive credit, as observed in Malaysia, Thailand, and Vietnam, where the credit-to-GDP ratio exceeded 100%, diminishes the positive impact of credit and renders it statistically insignificant. When the credit-to-GDP ratio surpasses a certain threshold, it can even negatively affect output growth (Lay 2020; Quoc and Duy 2025). Excessive credit growth may overheat the economy, triggering inflation that hinders sustainable growth. Misallocation of credit and inefficient financial intermediation may also explain why credit does not significantly enhance productivity. Credit directed more toward consumption than productive investment is unlikely to increase labor productivity.

The adverse effect of the age dependency ratio on productivity, operating through its moderating effect, may be explained by several factors. A higher proportion of the non-productive population (children and the elderly) requires the working-age population to allocate a larger share of their income to meet the needs of dependents, rather than saving or investing. A high dependency ratio can reduce the effective size of the labor force relative to the total population. This situation may result in labor shortages and a lower capacity for overall economic output and innovation if not managed effectively. Additionally, resources allocated to improving education and health for the working-age population may become constrained, potentially limiting the development of a high-quality workforce essential for sustainable economic growth.

This study also finds that government debt positively affects productivity. Government debt can finance productive investment, stimulate output, and enhance labor productivity. In contrast, broad money exerts a negative effect on productivity. A rising broad money-to-GDP ratio, particularly above 100 percent, can fuel inflation and hinder economic growth. High broad money-to-GDP ratios are observed in Malaysia and Thailand, while rapidly increasing ratios are seen in Vietnam and the Philippines.

This study investigates the effect of the age dependency ratio on labor productivity growth and examines its moderating role. The results show that the age dependency ratio significantly reduces the positive impact of FDI on productivity. Overall, the moderating effect of demographic factors, specifically the age dependency ratio, diminishes the effects of both FDI and private sector credit on labor productivity. The age dependency ratio exerts a stronger influence as a moderating variable than as an independent variable.

These findings provide further insight into how population age composition, as reflected in the age dependency ratio, affects productivity beyond the direct effects of FDI and credit. In ASEAN countries with large populations, demographic factors are critical in supporting economic growth. Increasing the productivity of the working-age population (ages 15–64) should be a top priority for addressing employment challenges. The government can enhance the quality and accessibility of education and training and increase employment by promoting investment. Policies aimed at boosting FDI inflows and private sector credit can stimulate investment, thereby raising aggregate labor productivity.

Furthermore, improving healthcare services, social security, and pension systems is essential for strengthening the productive-age population. In the long term, government policies on family planning and the prevention of early marriage should be given greater attention to manage population age structure and support sustainable economic growth.