Introduction

Attaining resilient economic expansion is crucial for enhancing quality of life and promoting overall affluence. This objective is largely dependent on the implementation of efficient macroeconomic strategies, where gross domestic product (GDP) plays a crucial role as a primary measure of a country’s economic progress (Jim-Suleiman & Adeyele, 2023; Mollah et al., 2022; Semuel & Nurina, 2015). However, realizing such growth involves navigating a complex interplay of several macroeconomic factors, including inflation, interest rates, exchange rates, foreign direct investment (FDI), and household spending (Anaripour, 2011; Ingham, 2013; Jakob, 2015; Kibria et al., 2014; Mamo, 2012; Svyrydenko et al., 2023; Tapsin & Hepsag, 2014; Zayed et al., 2022).

Inflation, in particular, carries significant implications for economic stability and growth, prompting policymakers to adopt strategies aimed at maintaining low inflation levels while fostering strong economic performance (Ayyoub et al., 2011; Zhao & Brychko, 2023, United Nations Conference on Trade and Development (UNCTAD), 2019). Yet the determinants of economic growth in transitioning and developing countries present unique challenges and complexities compared to their industrialized counterparts (Attari & Javed, 2013). Despite ongoing debates surrounding the optimal allocation of resources for innovation and development, empirical evidence remains limited, leaving critical questions unanswered (Ben Slimane & M’Henni, 2021; Mulska et al., 2022; Omelchuk et al., 2022; Petrova et al., 2020; Sakun et al., 2021; Semenets-Orlova et al., 2022b; United Nations Development Programme, 2020).

This study seeks to address these gaps by examining the economic growth trajectories of two emerging economies: Bangladesh and Turkey. These nations, with distinct demographic profiles and economic structures, offer valuable insights into the diverse pathways to economic development in the global arena. By analyzing the variations in economic determinants that influence per capita income growth, we aim to shed light on the disparities between middle-income countries and others, particularly regarding their growth prospects and underlying factors (Akyuz, 2019; Baharumshah et al., 2016; Eichengreen et al., 2018; European Training Foundation, 2011; FDI in Turkey, 2022; Haider, 2021).

The objective of investigating the determinants of economic growth in developing countries, particularly focusing on Turkey and Bangladesh, is justified by the need to understand the factors influencing economic performance in these contexts. The study employs a panel causality test to examine the relationships between various macroeconomic variables and economic growth, contributing to the existing literature by analyzing the impact of foreign direct investment (FDI) and offering insights into strategies for attracting capital and fostering long-term economic success in these nations.

The structure of the paper is as follows: the “Literature Review” section provides a comprehensive review of the literature, highlighting the gaps and setting the stage for our study. the “Methodology” section discusses the data sources, model specification, and methodology employed. The “Results” section presents the empirical results, followed by a detailed discussion in the “Discussion” section. Finally, the “Conclusion” section concludes the study, summarizing key findings and outlining avenues for future research.

Literature Review

The exploration of economic growth within developing nations constitutes a vibrant arena of academic and policy-oriented discourse, characterized by a deep dive into the intricate weave of macroeconomic dynamics, institutional frameworks, and socio-political landscapes. The seminal works of Acemoglu et al. (2005) and Kaufmann et al. (2011) have laid the groundwork, emphasizing the pivotal role of institutions and governance in shaping economic trajectories. This body of literature underscores the multifaceted nature of economic development, positing that growth is not merely a function of economic indicators but is deeply influenced by the quality of governance and institutional integrity (El-Gharmam, 2002; Petrova et al., 2020; Turkey Foreign Direct Investment, 2022; World Bank, 2018).

Our investigation draws inspiration from these foundational insights but seeks to venture beyond by delving into the nuanced interplay between macroeconomic factors and economic growth within the distinct contexts of Turkey and Bangladesh. Notably, the literature often treats these relationships in broad strokes, glossing over the subtleties that differentiate one developing economy from another. Rodrik (2018) and Eichengreen et al. (2018) have argued for a more nuanced understanding, pointing out the necessity to contextualize economic analyses within the specific socio-economic landscapes of nations under study.

Population growth and demographic shifts, for instance, are universally acknowledged as critical determinants of economic destiny (Bloom et al., 2014, 2015; Canning & Bloom, 2008; Peterson, 2017). However, the implications of these demographic dynamics are profoundly localized, presenting unique challenges and opportunities for sustainable development in Turkey and Bangladesh. Zeynalli and Rahimli (2022), Bazaluk et al. (2022), and Babu (2022) provide compelling arguments on the differential impacts of population growth, urging a more tailored analysis that considers the local socio-political context.

Furthermore, the investment in human capital through education emerges as a consensus driver of economic vibrancy across the literature (Greenaway et al., 2002; Hanushek & Woessmann, 2015; Teague, 2021; World Bank, 2018). Yet this narrative often overlooks the disparities in access and quality, which can exacerbate economic divides. Our study is thus compelled to explore how these disparities manifest in Turkey and Bangladesh, potentially offering fresh insights into bridging the education gap for economic empowerment (Hutorov et al., 2020; Semenets-Orlova et al., 2022a; Prahalad & Hammond, 2002; Chan, 2001; Asian Development Bank, 2020).

The mobilization of domestic and foreign investment underscores another dimension where existing literature, while acknowledging its importance, falls short of dissecting the localized mechanisms through which these investments spur growth in specific developing economies (UNCTAD, 2020). Our research is poised to address this gap, scrutinizing the role of foreign direct investment and its interplay with domestic investment dynamics in propelling Turkey and Bangladesh toward their growth aspirations.

Institutional quality and governance structures, reinforced by subsequent studies, are critical yet complex variables influencing economic outcomes (Mauro, 1998; FDI in Turkey hits $14.2B in 2021 to exceed pre-pandemic levels, 2022; Elahi, 2021; Eggoh et al., 2015; Bangladesh Foreign Direct Investment, 2022). This study intends to build upon this premise, examining how governance models in Turkey and Bangladesh facilitate or hinder economic growth, an area that remains ripe for deeper exploration.

Stages and Determinants of Growth Development in Bangladesh and Turkey

Bangladesh and Turkey are notable examples among developing nations. Bangladesh is experiencing a significant transition from its agricultural origins to a more varied economy, characterized by the expansion of its industrial and service sectors (Ahmed & Hossain). Meanwhile, Turkey is positioned as a vibrant developing market economy, utilizing its advantageous geographical location and varied industrial foundation to drive economic expansion and enhance competitiveness. In Bangladesh, the primary emphasis is on strengthening human resources through education and healthcare programs, while also taking advantage of export-driven economic growth, especially in industries such as textiles and apparel (Ahmed & Hossain; Rahman & Zaman, 2016). On the other hand, Turkey’s economic growth depends on actively promoting investment, fostering innovation, and developing infrastructure to attract foreign direct investment (FDI) and becoming part of global value chains. Moreover, Turkey’s advantageous geographical location as a transit hub not only strengthens its economic resilience but also contributes to its dynamism.

This improved literature assessment not only confirms the importance of macroeconomic variables and institutional frameworks in economic growth narratives but also establishes the foundation for the unique contribution of our study. By combining these overarching conceptual ideas with a detailed analysis of Turkey and Bangladesh, our objective is to reveal the precise methods by which economic growth can be maximized in these particular situations. The purposeful use of the Dumitrescu–Hurlin panel causality test as our methodology aims to capture the dynamic interaction between these factors, providing a complex analysis that surpasses the constraints of other studies (Yeon, 2021; Ali et al., 2023; Yuldashev et al., 2022).

Our research objective is to enhance our comprehension of economic development in underdeveloped economies by critically engaging with existing literature. This study aims to provide a meaningful empirical contribution to the existing literature on economic growth by conducting a detailed examination of Turkey and Bangladesh. The findings of this study will help inform and shape policy decisions in a more informed and contextually appropriate manner.

Addressing Gaps in the Literature and Research Agenda

Despite the extensive body of literature delving into the dynamics of economic growth, there are discernible gaps, particularly concerning the intricate interplay of macroeconomic factors within the unique socio-economic landscapes of emerging economies like Turkey and Bangladesh (Eichengreen et al., 2018; Rodrik, 2018). Prior studies, while informative, have often taken a generalized approach that fails to fully capture the idiosyncratic experiences of these nations, resulting in a homogenized perspective that oversimplifies the complexities of individual country scenarios (Acemoglu et al., 2005; Kaufmann et al., 2011; Nourzad, 2002).

Our comprehensive literature review identifies a pressing need for a more profound analysis that transcends the conventional employment of broad economic models, thereby addressing the first identified gap. This entails a meticulous examination of the causal relationships between macroeconomic indicators—such as FDI, inflation, and population growth—and economic outcomes within the specific institutional frameworks of Turkey and Bangladesh, a perspective that has been notably underexplored (UNCTAD, 2020; World Bank, 2018).

Furthermore, existing literature often implies a linear association between economic variables and growth, overlooking the potential for non-linear dynamics and threshold effects that could significantly reshape our understanding of these relationships (Imbens & Rubin, 2015; Oetomo & Santoso, 2023; Wooldridge, 2010). To fill this critical void, our study endeavors to delve deeper into these intricate, non-linear relationships using advanced econometric techniques, notably the Dumitrescu–Hurlin panel causality test, thereby tackling the second identified gap (Dumitrescu & Hurlin, 2012).

The selection of the Dumitrescu–Hurlin test is grounded in its capability to detect causality in panel data models, making it ideally suited for unraveling the bidirectional feedback between the economic variables under scrutiny (Dumitrescu & Hurlin, 2012). This methodological choice diverges from prior static analyses prevalent in the literature, offering a more nuanced exploration of the dynamic interplay of economic indicators (Bilgili & Ozturk, 2015; Colakoğlu, 2019).

By addressing these prominent gaps, our research propels the discourse beyond static and oversimplified economic analyses, contributing to a more sophisticated understanding imperative for devising nuanced policy interventions. The implications of such an endeavor are far-reaching, laying a strategic groundwork for sustainable economic development not only in Turkey and Bangladesh but conceivably in other developing nations grappling with similar economic challenges (Sen, 2019; Stiglitz, 2007).

In summation, our research agenda aims to construct a nuanced narrative of economic development, one that acknowledges the divergent paths traversed by emerging economies. Through our scholarly pursuits, we aspire to furnish substantive empirical evidence that informs a precise and differentiated policy framework in the pursuit of economic growth and development (Rajan, 2019; OECD, 2018b; El-Ghannam, 2002; Currie, 2009; Groth, 2010; Brynjolfsson & McAfee, 2014).

Methodology

Discussion of the Variables and Model

According to the World Bank’s classification of countries by income level, Turkey is considered a higher-middle-income developing country, while the People’s Republic of Bangladesh is classified as a lower-middle-income developing country. The World Bank’s classification is based on gross national income (GNI) per capita, and it is reviewed every July. The classification made by the World Bank is based on two factors: Inflation, exchange rates, and population growth can affect GNI per capita. If there are revisions to the national account’s method and data, the GNI may change. To maintain consistency, the annual adjustment for inflation is made to the income classification thresholds (Table 1).

Table 1 Economic status classifications
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The selection of Turkey and Bangladesh as the countries of interest in this research is based on their income classifications according to the World Bank. Turkey falls under the higher-middle-income group, while Bangladesh is classified as a lower-middle-income country. These classifications are determined based on the countries’ gross national income (GNI) per capita, which is regularly reviewed and adjusted for factors such as inflation, exchange rates, and population growth. By focusing on these two countries, we aim to examine the relationship between key macroeconomic variables and economic growth. The research employs panel analysis, which allows for a comprehensive examination of the variables over a specific time frame. The dataset used in this study is sourced from the World Bank and covers the period from 1981 to 2020. Through econometric analysis and statistical tests, we seek to explore the causal relationship between various macroeconomic indicators and economic development in Turkey and Bangladesh. By analyzing the data in a panel framework, we can account for both within-country variations and cross-country differences, providing a more robust analysis of the factors influencing economic growth in these two nations.

Variables and Model Description

The proxy for economic growth (Y) used in the study was the growth rate of GDP per capita, which was the dependent variable. The indicator for this variable in the model was “GROWTH.” It represented the percentage change in per capita GDP from 1 year to the next. For example, the GDP per capita growth for the year 2010 was the rate of change of GDP per capita from 1981 to 2020. As countries can experience both negative and positive growth in different periods, this rate could be either negative or positive. A higher growth rate was considered desirable.

$$Y\mathit=\mathit\;f\mathit\;\mathit{\left({Pop\_growth,\;Inf,\;Lit\_rate,\;Res,\;Invest,\;Fd\_in,\;Fd\_out}\right)}$$
(1)

The first variable used is the population, which is the total number of inhabitants based on the de facto count, including all residents regardless of legal status or citizenship in Turkey and Bangladesh. The data used was the population growth information obtained from the World Bank for the period of 1981–2020, with the unit of measurement used as a percentage. The expected correlation of the coefficient was positive.

The second variable was inflation, which is the rate of increase in prices. The data used was the GDP deflator, which compares the current rate of GDP to a constant price. The data was obtained from the World Bank for the period of 1981–2020, with the unit of measurement used as a percentage. The expected correlation of the coefficient was negative. The third variable was the literacy rate, which is a widely recognized factor in economic growth. Data were obtained from the World Bank for the period of 1981–2020, with the unit of measurement used as a percentage. The expected correlation of the coefficient was positive.

The fourth variable was the natural resources produced by the country for domestic use or export, represented by RESOURCE. The unit of measurement for this variable was the dollar. There is an ongoing debate about whether the production and export of natural resources have positive or negative impacts on economic growth. The predicted correlation of the RESOURCE coefficient was previously uncertain, as previous studies have shown that the export of resources can either have a positive effect, contributing to the growth of a country, or a negative effect, due to the Dutch Disease and rent-seeking.

The fifth variable was the country’s investment, represented by INVEST. The unit of measurement for INVEST was the dollar. It represented the amount spent on the creation of capital goods. Historically, the expected correlation of the INVEST coefficient was positive. Previously, the sixth variable was the inflow of foreign direct investment (FDI) into the country. The unit of measurement for FDI was in dollars. It represented the amount invested by foreign entities or organizations in the local economy. As a result, the predicted correlation of the FDI coefficient was positive.

The seventh set of two variables in the table was the inflow of foreign direct investment (FDI) into the country, which was denoted as FDI. The unit of measurement for FDI was in dollars. It represented the amount invested by the local economy in foreign entities or businesses. As a result, the predicted correlation of the FDI coefficient could be either positive or negative (Table 2).

Table 2 Details of the variables used, including their descriptions, units of measurement, and expected signs of correlation
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Explicitly, in an estimable form, Eq. (1) is re-written as.

$$\begin{aligned}{\mathrm Y}_{\mathrm{it}\;\mathrm G}=&\;{\mathrm\beta}_{\mathrm i0}+{\mathrm\beta}_{\mathrm i1}\;{\mathrm{Pop}\_\mathrm{growth}}_{\mathrm{it}}\\&+{\mathrm\beta}_{\mathrm i2}\;{\mathrm{Inf}}_{\mathrm{it}}+{\mathrm\beta}_{\mathrm i3}\;{\mathrm{Lit}\_\mathrm{rate}}_{\mathrm{it}}\\&+{\mathrm\beta}_{\mathrm i4}\;{\mathrm{Res}}_{\mathrm{it}}+{\mathrm\beta}_{\mathrm i5}\;{\mathrm{Invest}}_{\mathrm{it}}\\&+{\mathrm\beta}_{\mathrm i6}\;{\mathrm{Fd}\_\mathrm{in}}_{\mathrm{it}}+{\mathrm{Fd}\_\mathrm{out}}_{\mathrm{it}}+{\mathrm\varepsilon}_{\mathrm{it}}\end{aligned}$$

As part of our research objective to investigate the factors influencing GDP growth, we formulated the following hypothesis: hypothesis (H1): There exists a significant relationship between population growth, literacy rate, resource availability, investment, foreign direct investment inflows, and foreign direct investment outflows and their impact on GDP growth. By examining the interplay between these variables, we aim to shed light on the extent to which population growth, literacy rate, resource utilization, investment levels, and foreign direct investment contribute to the overall economic growth represented by GDP.

Model Estimation

A cross-country growth analysis will be performed via panel data of category-wise distinct countries, which will pertain to the lookup question. Based on some specific indicators, exclusive kinds of evaluation will be done, and the time frame is 1981 to 2020. Among the analyses, there will be the econometric evaluation of panel data, econometric methods, cross-country analysis, empirical analysis, cross-country growth regressions, time series analysis, cross-section dependence, cross-section dependence, slope homogeneity test, panel cointegration test, panel causality, and slope homogeneity test to be performed to check out the statistics for financial growth. Moreover, in this study, the specification of the relationship between a range of elements and improvement will be showcased. The CCEMG method produces results that are specific to both the panel and individual countries. To evaluate the stability of the variables in our model, we employ Pesaran’s panel unit root test (2007). The test statistic, known as the CIPS, is obtained by averaging the CADF values across individual cross-sections. We employ Pesaran’s CCEMG approach to examine the relationship between dependent and independent variables (2006).

As a summary of this research, we will practice for panel cointegration using common correlated effect (CCE) by way of Pesaran’s (2006) cross-section dependence test and then CIPS West 2007 cointegration test (for unit root and cointegration), and we use some other checks in accordance with our research demand.

Results

Summary Statistics

This study investigated the panel framework which specifies the relationship between the GDP, population growth, inflation, literacy rate, natural resources, net investment, FDI inflow, and FDI outflow for Bangladesh and Turkey.

$$\begin{aligned}{\mathrm{GDP}}_{\mathrm{it}}=&\;{\mathrm\beta}_{\mathrm i0}+{\mathrm\beta}_{\mathrm i1}\;{\mathrm{Pop_growth}}_{\mathrm{it}}\\&+{\mathrm\beta}_{\mathrm i2}\inf\nolimits_{\mathrm{it}}+{\mathrm\beta}_{\mathrm i3}{\mathrm{Lit_rate}}_{\mathrm{it}}\\&+{\mathrm\beta}_{\mathrm i4}{\mathrm{Res}}_{\mathrm{it}}+{\mathrm\beta}_{\mathrm i5}\;{\mathrm{Invest}}_{\mathrm{it}}\\&+{\mathrm\beta}_{\mathrm i6}\;{\mathrm{Fdi_in}}_{\mathrm{it}}+{\mathrm\beta}_{\mathrm i7}\;{\mathrm{Fdi_out}}_{\mathrm{it}}+{\mathrm\varepsilon}_{\mathrm{it}}\end{aligned}$$

where t = 1981, 1991 … 2020 and i = 1, 2 (Turkey and Bangladesh). The subscripts I and t in this model stand for individual (country) and time, respectively. The intercept and disturbance are displayed by βi0 and εit. βi1, βi2, βi3, βi4, βi5, βi6, and βi7 are the coefficients of population growth, inflation, literature rate, natural resource, investment, and FDI inflow FDI outflow, respectively.

In Table 3, we present the descriptive panel statistics for both Bangladesh and Turkey, providing an overview of the variables under study. The mean GDP for the two countries is 3733.663, with a standard deviation of 4050.042. The minimum recorded GDP is 247.6496, while the maximum is 12,614.78. This highlights the variability in economic output between the two nations. Population growth, on average, stands at 1.490254, with a standard deviation of 0.3412319. The minimum and maximum population growth rates observed are 0.780048 and 2.126059, respectively. These figures reflect the varying rates of population expansion in the two countries.

Table 3 Descriptive panel statistics for both Bangladesh and Turkey
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Inflation exhibits a mean value of 20.97126, accompanied by a standard deviation of 27.35334. The lowest recorded inflation rate is 2.007174, while the highest is 105.215. This indicates substantial fluctuations in price levels over the studied period. The literacy rate, representing the percentage of the population with basic education, has an average value of 77.92732 and a standard deviation of 17.84479. The minimum and maximum literacy rates observed are 35.3193 and 96.74221, respectively, indicating variations in educational attainment between the two countries. Natural resource availability, measured on a scale from 0 to 1, exhibits a mean value of 0.612612, with a standard deviation of 0.3688673. The minimum and maximum recorded values are 0.139736 and 1.646285, respectively, suggesting differences in the abundance of natural resources.

Net investment in a nonfinancial asset has a mean value of 1.940582, with a standard deviation of 0.6231481. The range of net investment spans from 1.076646 to 4.392464, indicating variations in investment levels between the two countries. FDI inflow, representing foreign direct investment entering the countries, has an average value of 0.897371, with a standard deviation of 0.7787503. The minimum and maximum FDI inflow values recorded are 0.004492 and 3.623502, respectively, showcasing the varying levels of foreign investment. FDI outflow, representing foreign direct investment leaving the countries, exhibits a mean value of 0.142926, accompanied by a standard deviation of 0.1683906. The FDI outflow ranges from 0 to 0.750837, reflecting differences in outward foreign investment. These summary statistics provide a snapshot of the key variables and their distribution in both Bangladesh and Turkey. They serve as the foundation for our analysis of the relationships between GDP, population growth, inflation, literacy rate, natural resource availability, net investment, FDI inflow, and FDI outflow in the panel framework.

Panel Unit Root Test Results

To assess the stationarity of the variables in our study, we conducted panel unit root tests using Pesaran’s (2007) methodology. The results, presented in Table 4, provide insights into the cross-section dependence between the two countries (BD and Turkey) and the integration properties of the variables. For most variables, the panel unit root tests do not reject the null hypothesis of a unit root in intercept and trend. This indicates that these variables exhibit non-stationarity and have order one integration. However, it is worth noting that the FDI variable stands out as an exception, as it rejects the null hypothesis of a unit root in both intercept and trend. The results reveal important information about the integration properties of the variables. For example, population growth, inflation, GDP, exports, and natural resource rent all exhibit strong cross-section dependence, as indicated by the significant LM_AD intercept and LM_AD intercept + trend statistics. This suggests that these variables are influenced by common factors that affect both countries.

Table 4 BD and Turkey cross-section dependence and panel unit root tests
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On the other hand, variables such as literacy rate, investment, FDI inflows, and FDI outflows do not show significant cross-section dependence, as reflected by non-significant LM_AD statistics. This implies that these variables may be driven by country-specific factors rather than common factors shared by both countries. Overall, the panel unit root tests help us understand the integration properties of the variables and the presence of cross-section dependence between BD and Turkey. These findings contribute to our analysis of the data and provide insights into the dynamics of the variables under consideration.

Dumitrescu–Hurlin Panel Causality Results

The Dumitrescu and Hurlin panel causality test was employed to examine the relationships between variables in our study. The results, presented in Table 5, provide valuable insights into the causal dynamics between population growth, GDP, inflation, and other variables. First, we found a significant unidirectional relationship between population growth and GDP. This suggests that changes in the population size have a substantial impact on economic output. As the population increases, there is a corresponding positive effect on GDP.

Table 5 Pairwise panel causality tests results between two countries
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Additionally, population growth also exhibited a significant causal association with inflation. This implies that changes in population size can influence the inflation rate of a country. Understanding this relationship is crucial for policymakers in implementing effective monetary and fiscal policies. On the other hand, we did not identify any significant unidirectional causality between inflation and the other variables under consideration. This suggests that inflation does not have a substantial direct impact on GDP, population growth, or gross national income per capita. Overall, the Dumitrescu–Hurlin panel causality results shed light on the intricate relationships between population growth, GDP, inflation, and other variables. These findings contribute to our understanding of the macroeconomic dynamics and can inform policymakers in formulating strategies to foster sustainable economic growth and manage inflation effectively.

Trend Analysis of Turkey and Bangladesh

The trend analysis depicted in Fig. 1 highlights the population dynamics of both Turkey and Bangladesh over the years. It is observed that the population of both countries has shown a consistent downward trend since 1990. However, a notable deviation in the trend is observed for Bangladesh during the early 2000s, where a steep decline in population is evident until 2010. Furthermore, the population growth rate of both countries follows a similar trend, indicating a parallel pattern in terms of population dynamics. This analysis provides valuable insights into the long-term population trends of Turkey and Bangladesh, emphasizing the need to examine the factors contributing to the observed population changes and their potential implications for various socio-economic aspects in these countries (Fig. 2).

Fig. 1
figure 1

Trend analysis

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Fig. 2
figure 2

Difference between the inflation rates

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A noticeable disparity is observed in the inflation trends of the two countries. Starting from 1990, Bangladesh experienced relatively stable inflation below 10%. In contrast, Turkey faced a significant challenge in 1990, with an inflation rate reaching nearly 60%, indicating a period of hyperinflation. However, Turkey managed to reduce the inflation rate gradually, reaching around 50% by the early 2000s and further declining to approximately 10% by 2010. During the same period, Bangladesh also witnessed a similar inflation rate, slightly higher than in previous years, primarily attributed to the global market crisis that affected economies worldwide. From 2010 to 2020, Turkey’s inflation rate began to rise again, while Bangladesh successfully maintained a lower inflation rate. This analysis underscores the distinct inflationary experiences of Bangladesh and Turkey, highlighting the significant efforts made by each country to manage inflation and stabilize their respective economies. Understanding these inflation trends provides valuable insights for policymakers and researchers in assessing the effectiveness of measures implemented to control inflation and promote economic stability (Fig. 3).

Fig. 3
figure 3

Difference between the total debits

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Starting from 1990, Bangladesh witnessed a significant decline in its total debt, demonstrating a remarkable downward trend for the next decade until the early 2000s. In contrast, Turkey’s total debt fluctuated within the range of $30–40B. In 2010, Bangladesh managed to slightly reduce its total debt, but thereafter, it experienced a slight increase, reaching a level slightly higher than $10B.

This analysis sheds light on the contrasting patterns in the total debt trajectories of Bangladesh and Turkey. While Bangladesh showcased a significant reduction in total debt during the 1990s and early 2000s, Turkey’s total debt exhibited relatively stable levels. Understanding these variations in total debt provides insights into the debt management strategies and fiscal policies adopted by each country. Policymakers and researchers can utilize this information to assess the impact of debt reduction efforts and formulate effective debt management strategies for sustainable economic growth (Fig. 4).

Fig. 4
figure 4

Trend analysis of population growth

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It is evident from the graph that Bangladesh has consistently experienced higher population growth compared to Turkey. However, in 2020, the population growth in Bangladesh was lower than in Turkey. In terms of export performance, Turkey outperformed Bangladesh in 2020, as indicated by higher export levels. This suggests that Turkey had a stronger presence in international trade compared to Bangladesh in that particular year. The inflation rate of Bangladesh has shown relative consistency over time, with only minor fluctuations. In contrast, Turkey witnessed a significant decline in inflation, leading to lower inflation rates compared to Bangladesh. In terms of gross national income (GNI) per capita, Turkey had a lower value in 2020 compared to Bangladesh. This implies that on average, each individual in Turkey had a lower income level compared to individuals in Bangladesh in that particular year. The analysis of these trends provides valuable insights into the demographic dynamics, trade performance, inflation patterns, and income levels of Bangladesh and Turkey. Policymakers and researchers can utilize this information to evaluate the economic conditions and formulate appropriate strategies to address specific challenges and promote sustainable development in each country.

Discussion

The study’s findings support previous research indicating a significant influence of population growth on economic output (Acemoglu et al., 2005; Ahmed & Hossain, 2023; Alam et al., 2023; Hania & Anis, 2023). The observed unidirectional causality from population growth to GDP underscores the pivotal role of demographic changes in shaping economic development trajectories in both Bangladesh and Turkey, emphasizing the necessity of policies addressing population dynamics to foster sustainable growth. Additionally, the study confirms the significant causal relationship between population growth and inflation, aligning with existing literature (Canning & Bloom, 2008) and emphasizing the importance of understanding demographic trends in inflation dynamics for effective policymaking.

Contrary to some prior studies, the research did not identify a direct impact of inflation on GDP or population growth, highlighting the complexity of inflation’s effects on broader economic outcomes and the necessity for nuanced analyses considering country-specific factors. The study’s trend analysis reveals distinct economic trajectories between Bangladesh and Turkey, with Bangladesh demonstrating stable inflation rates and debt reduction efforts, while Turkey has faced inflationary challenges but has made efforts to stabilize prices and manage debt levels (Asian Development Bank, 2020; OECD, 2018a).

Moreover, the analysis highlights divergent population growth patterns between the two countries, with Bangladesh experiencing consistently higher growth rates while Turkey demonstrates stronger export performance and lower inflation rates, indicating the necessity for tailored policy responses to address unique socio-economic challenges in each context.

Overall, the study contributes empirical evidence on causal relationships between key macroeconomic variables in Bangladesh and Turkey, offering insights into complex interactions shaping economic outcomes. By contextualizing findings within broader theoretical frameworks and comparing them with existing literature, the study enriches scholarly discourse on economic development and informs evidence-based policymaking for sustainable and inclusive growth in both countries and beyond.

The findings of this study provide valuable insights into the complex relationships between various macroeconomic indicators and GDP growth in Turkey and Bangladesh. Through rigorous analysis, we have uncovered significant causal relationships that have important implications for economic policymaking and development strategies in these countries.

Firstly, our study reaffirms the negative impact of population growth on GDP growth, consistent with prior research (Romer, 1996; Bazaluk et al., 2024; Mia et al., 2023; Kader et al., 2022; Islam et al., 2021; Watts, 2002a, b; Kader et al., 2020; Islam, 2020). This underscores the urgent need for effective population control measures to mitigate the adverse effects of demographic pressures on economic development. Policymakers must prioritize investments in family planning, healthcare, and education to address population growth challenges and promote sustainable economic growth.

Moreover, our analysis highlights the positive relationship between investment and GDP growth in both countries. Increased investment, including domestic and foreign direct investment (FDI), plays a crucial role in stimulating economic activity, enhancing productivity, and fostering technological innovation. To capitalize on this potential, policymakers should focus on creating a conducive investment climate, improving infrastructure, and implementing targeted incentives to attract investment in key sectors.

Furthermore, our findings reveal important insights into the relationship between inflation and GDP growth. While a negative relationship is observed in Bangladesh, no significant relationship is found in Turkey. This discrepancy may be attributed to differences in monetary policy frameworks and inflation-targeting strategies. Future research should delve deeper into these differences to inform more nuanced policy interventions aimed at addressing inflationary pressures while promoting sustainable economic growth.

Additionally, our analysis underscores the importance of sustainable resource management practices in harnessing the potential benefits of natural resources. While natural resource abundance can contribute to economic growth, excessive reliance on resource extraction may lead to depletion and environmental degradation. Policymakers must adopt strategies to promote sustainable resource utilization and diversify the economy to reduce dependence on finite resources.

Moreover, our study emphasizes the critical role of human capital development in driving long-term economic growth. Higher literacy rates and investments in education are associated with increased productivity, innovation, and entrepreneurship, which are essential for enhancing competitiveness and fostering sustainable development. Policymakers should prioritize investments in education and skill development to unlock the full potential of their human capital.

In conclusion, our study provides valuable insights for policymakers, economists, and development practitioners in Turkey, Bangladesh, and beyond. By identifying the key determinants of economic growth and their interrelationships, this study informs evidence-based policymaking and development strategies aimed at promoting inclusive and sustainable growth. However, it is essential to acknowledge the limitations of our study, including data constraints and the complexity of macroeconomic dynamics. Future research should build upon our findings and explore additional factors shaping economic development to generate robust policy recommendations for fostering prosperity and well-being in emerging economies.

Conclusion

This study aimed to scrutinize the significant influences of various macroeconomic indicators on GDP growth in Turkey and Bangladesh. Through an in-depth analysis of population growth, literacy rates, resource rent, investment, foreign direct investment (FDI) inflows, FDI outflows, and inflation, we have gleaned valuable insights into the determinants of economic growth in these two countries. The findings underscore several critical relationships between the examined variables and GDP growth. Firstly, population growth emerges as a pivotal factor influencing GDP growth, highlighting the substantial contribution of demographic dynamics to economic expansion. However, it is imperative to note that while population growth can fuel economic growth, sustainable population management strategies are indispensable to ensure long-term development sustainability.

Secondly, the study highlights the indispensable role of investment, particularly foreign direct investment, in stimulating economic growth. The inflow of FDI exhibits a positive association with GDP growth, indicating the significance of attracting foreign capital to propel economic development. Additionally, domestic investment plays a pivotal role in enhancing productivity, fostering innovation, and creating employment opportunities, thereby contributing to overall economic growth. Furthermore, the analysis emphasizes the positive correlation between literacy rates and GDP growth, underlining the significance of human capital development in driving economic progress. Higher levels of education correlate with increased productivity, innovation, and entrepreneurship, underscoring the importance of investing in education and skills development to sustain economic growth.

In contrast, the study does not find a significant influence of inflation on GDP growth in the examined countries. While inflation may impact economic stability and purchasing power, other factors such as investment and demographic trends appear to exert greater influence on GDP growth. The results of the causality test support the hypothesis that GDP growth unidirectionally affects most of the examined macroeconomic indicators. This suggests that robust GDP growth can drive improvements in population dynamics, literacy rates, and investment levels, among other economic factors.

However, it is essential to acknowledge the limitations of this study. The analysis is based on data from only two countries, which may limit the generalizability of the findings. Moreover, the study focuses on a specific set of macroeconomic factors, excluding other variables that could also influence GDP growth. Future research could explore additional factors and employ a broader sample to validate and extend the findings of this study. In conclusion, this research contributes to our understanding of the determinants of GDP growth and provides valuable insights for policymakers, investors, and other stakeholders. By identifying the key factors driving economic development in Turkey and Bangladesh, this study informs evidence-based decision-making aimed at promoting sustainable and inclusive growth.