Despite the growing emphasis on the nexus between growth and macroeconomic indicators­, research on the influence of cryptocurrencies on economic performance remains limited. This study compares the impact of two leading cryptocurrencies, Bitcoin and Ethereum, on economic growth, alongside inflation, market uncertainty, and oil and gold prices, using panel data from 14 countries between Q3 2015 and Q3 2023. The results demonstrate robust cross-sectional dependence, indicating that economic shocks in one country affect the entire group. Therefore, second-generation tests are employed to confirm the presence of stationarity in the variables. Except for Bitcoin’s trading volume, panel fully modified ordinary least squares estimations reveal a significantly positive impact of cryptocurrencies on growth. Cointegration is present in the long run, while in the short run, strong bi- and unidirectional causality is found for all cryptocurrency proxies. The study provides insights that can help policymakers develop strategies to align economic growth with the crypto market, benefiting the broader economy.

cryptocurrency, economic growth, panel data models, Bitcoin, Ethereum

The digital transformation of the financial system spurred the creation of decentralized digital currencies known as cryptocurrencies. Nakamoto (2008) introduced the concept of a cryptographic-based electronic payment system to replace the trust-based model of traditional banking. The current financial system was criticized for its costliness, inefficiency, and transaction limits. Therefore, Bitcoin (BTC), a virtual currency and a payment form without third-party involvement, was introduced. Since then, BTC’s success has driven the development of numerous other cryptocurrencies and blockchain projects (Joshi et al., 2018). Correspondingly, the number of cryptocurrencies has grown significantly since 2013, reaching nearly 20,000 by 2024 (Best, 2024).

Many cryptocurrencies were designed to replace the traditional financial system (Vora, 2015) but are often viewed by investors as speculative investment tools rather than innovative payment methods due to their high returns, decentralize­d nature, and underlying blockchain technology. Furthermore, the volatility of the crypto market created a suitable environment for speculators, providing opportunities to gain profits within short periods despite significant risks.

Even with this backdrop, cryptocurrencies also serve purposes beyond investment. In El Salvador, the government adopted them as legal tender to stimulate growth and address high public debt. El Salvador’s president, Bukele, stated that BTC adoption would encourage economic development and boost innovation and financial inclusion (Alfaro et al., 2022). China, by contrast, has banned crypto­currencies due to security concerns and the perception that they pose a threat to the monetary system (Wulandari, 2022). There is still no global consensus on the reliability of cryptocurrencies, as regulations remain ambiguous and their economic effects uncertain. As a result, some countries consider them a solution and a growth driver, while others regard them as a major risk and a potential source of financial instability. Given this global uncertainty, we analyze the impact of the two leading cryptocurrencies, BTC and Ethereum (ETH), on economic indicators within a multi-country framework. In doing so, it is important to acknowledge the contradicting results in the existing literature regarding whether cryptocurrencies affect economic growth.

To clarify the ambiguity in prior studies, this paper addresses the following question: Do cryptocurrencies have a substantial effect on economic growth in the long run? To this end, we first adopt panel fully modified ordinary least squares (FMOLS) due to its superior properties in handling complex datasets compared to time series models. Specifically, we employ panel cointegration tests to capture long-term relationships and panel Granger causality tests to identify short-term effects of both BTC and ETH on growth. Second, given the lack of comparable multi-country studies, we analyze 14 countries, systematically discussing the determinants of growth by integrating key economic, crypto-related, and financial market indicators. Considering the disparities among these 14 countries, analyzing the impact of cryptocurrencies on growth provides a broader and more reliable perspective than single-country analyses. Third, while most studies have focused solely on BTC or on macroeconomic determinants affecting it (Panagiotidis et al., 2018; Shaikh 2020; Vo et al., 2021), we also include ETH — the second most valuable and dominant cryptocurrency. As of December 2023, BTC and ETH accounted for more than half of the crypto market, with a combined dominance of 69%. Their respective market prices reached approximately $43,060 and $2,250.¹ To our knowledge, this is the first study that jointly examines both leading cryptocurrencies and their impacts on economic growth within a multi-country analysis.

The remainder of this paper is structured as follows: Section 2 presents a comprehensive review of the existing literature; Section 3 describes the data and methodology; Section 4 reports and discusses the empirical results; and Section 5 concludes the study.

The rapid development of cryptocurrency has attracted increasing attention from scholars over the last decade, making it important to investigate the inter­action between the crypto market and the broader economy. Earlier studies focused primarily on exploring the conceptual foundations and technological design of cryptocurrency systems. However, with the rising number of cryptocurrency users, attention has shifted toward examining the economic implications of the crypto market.

Most research has centered on BTC as a representative of the cryptocurrency market, since it was the first cryptocurrency introduced and remains dominant due to its popularity, high market capitalization, and trading volume. Although BTC’s dominance in the cryptocurrency market is undisputed, there is a growing need for more studies on ETH, the second most traded cryptocurrency.

Fig. 1 illustrates the rate of return (RoR, %) and trading volumes for BTC and ETH from Q3 2015 to Q3 2023, highlighting the high volatility and speculative nature of these assets. Many investors rely on RoR to assess investment efficiency and profitability, and it plays a significant role in shaping investment decisions and allocation of capital. The RoR graph reveals substantial fluctuations, with notable­ peaks in Q4 2017 Q1 and 2021 indicating sharp price increases. Conversely, periods­ of negative returns appear around Q1 2018 and Q2 2022, reflecting major value declines. In 2022, as central banks worldwide raised interest rates to curb inflation, investor appetite for speculative and risky assets diminished. Consequently, demand for BTC and ETH fell, leading to a decline in their prices and a negative RoR. These fluctuations underscore the inherent risk — return dynamics of the cryptocurrency market, where steep gains are often followed by abrupt corrections.

Fig. 1.

Trends in BTC and ETH returns and trading volumes from 2015 Q3 to 2023 Q3. Source: Compiled by the authors.

The trading-volume graph shows a significant spike in early 2021­. This surge was followed by a marked decline in trading activity after 2021 — ­approximately 30% for BTC and 23% for ETH. The downturn coincides with periods of lower RoR, suggesting that reduced returns disincentivized trading activity. These observations highlight the close linkage between trading activity and price movements, emphasizing the speculative and reactive nature of the cryptocurrency market.

The volatility of BTC and ETH prices, trading volumes, and returns — ­together with their rising popularity and market capitalization — has intensified academic­ interest in understanding their potential effects on the economy. Whether the crypto market plays a significant role in fostering economic growth and financial stability remains an open question. Existing studies on the relationship between cryptocurrencies and the economy employ diverse economic indicators and econometric models, often yielding contradictory results.

The purpose of this study is to investigate whether cryptocurrencies have a notable effect on countries’ economic growth. Furthermore, it examines the relationships among various economic and financial indicators and cryptocurrencies, and their combined effect on the economy. The dataset covers 14 countries from Q3 2015 to Q3 2023.

The country selection was informed by prior studies in the field. While Panigrahi (2023) focused exclusively on time-series data for India, Miśkiewicz et al. (2022) analyzed a panel of seven countries. To broaden these approaches, we assembled a heterogeneous panel dataset including 14 countries. This wider scope provides a stronger empirical basis for analysis and enhances the generalizability of the results, thereby strengthening the study’s contribution to the literature.

To estimate the panel data regression, we apply the group-mean fully modified ordinary least squares (GM-FMOLS) cointegration model, along with several pre- and post-estimation tests: cross-sectional dependence, unit-root, cointegration, and panel Granger causality tests. Since BTC and ETH hold the largest market shares in the crypto market, their trading volumes and rates of return are used as the main cryptocurrency indicators.

Real GDP growth serves as the dependent variable and proxy for economic growth. The RoR and trading volumes of BTC and ETH are selected as crypto-related variables. In addition, the consumer price index (CPI) measures inflation, VIX represents financial-market uncertainty, and both WTI and GOLD capture commodity-market dynamics. Table 1 presents a summary of the selected variables. The selection of these macroeconomic indicators was guided by their relevance to the research objectives and their ability to capture key economic dimensions that influence — and are influenced by — the activities of economic agents, including those engaging in cryptocurrency transactions. This selection also aligns with earlier research, particularly Panigrahi (2023), who employed similar indicators.

Accurate measurement of growth is essential. Although several forms of GDP are used in the literature, this study employs the real GDP growth rate, consistent with contemporary research (Evans, 2019; Jati et al., 2022; Chatterjee, 2023).

CPI provides key information on price changes that governments and firms use to make informed economic decisions (U.S. Bureau of Labor Statistics, 2023). In addition to serving as an inflation gauge, it can also indicate the effectiveness of government economic policy. The choice of CPI as the inflation proxy follows prior studies (Kremer et al., 2012; Barro, 2013; Saymehet et al., 2013; Evans, 2019).

VIX is a real-time market indicator that forecasts volatility expectations for the next 30 days based on S&P 500 option prices. As the S&P 500 is widely regarded as a leading barometer of global financial conditions, VIX (% change) is used here to represent both stock-market volatility and risk.

WTI is included as a benchmark for global crude-oil price changes, given its importance for oil-exporting countries such as Canada, Colombia, Indonesia, Saudi Arabia, and the United States, all of which are part of the dataset. Together with gold, the percentage change in WTI spot price serves as a commodity-market indicator; a positive correlation between WTI and growth is expected (Chatterjee, 2023).

Among the 14 countries in our dataset, seven (Australia, Canada, China, Colombia, Indonesia, South Africa, and the United States) have significant gold-mining and export sectors. The relationship between growth and gold prices is often inverse: during periods of strong economic expansion, investors prefer higher-return assets, reducing demand for gold and lowering its price (Wang and Lee, 2016). Conversely, gold acts as a safe-haven asset during downturns, increasing both demand and prices (Triki and Ben Maatoug, 2021). These dynamics highlight gold’s role as a hedge against macroeconomic instability and currency depreciation, making it a key variable for understanding economic sentiment.

The descriptive statistics for the selected variables are presented in Table 2. ETHV exhibits the largest mean value (approximately 1.75) compared with BTCV (0.94), and both variables display the highest standard deviations among the sample. The standard deviation of BTCR is lower than that of ETHR, indicating that Ethereum is riskier to invest in than Bitcoin. However, the maximum value of ETHR is substantially higher than that of BTCR, which may make Ethereum more attractive to certain investors seeking higher potential returns. The Jarque–Bera test results indicate that the data for all variables are normally distributed, except for GOLD.

Panel data are the most suitable for this study, as they provide greater variability and reliability than either cross-sectional or time-series data alone. Panel regression captures both the time dimension and the cross-country differences within the sample (Zulfikar, 2018), allowing us to address the research question effectively. This approach also controls for unobserved heterogeneity, such as country-specific characteristics — including economic structure, geographic location, and institutional classification — that could otherwise bias the results.

The general panel regression model can be expressed as follows:

Y_it = α_i + β₁ X_1it + β₂ X_2it + … + β_n X_3nit + ε_it , (1)

where Y_it represents the dependent variable (GDP growth), and α_i is the country-specific intercept. β₁, β₂, …, β_n denote the coefficients measuring the change in Y_it resulting from a one-unit change in the respective explanatory variables X_1it, X_2it, …, X_3nit. The error term ε_it captures random disturbances not explained by the model. Index i refers to cross-sections (i = 1, 2, …, n), and t denotes time periods (t = 1, 2, …, T).

Based on this framework, six models (Models 1–6) are constructed, each with different combinations of cryptocurrency indicators to compare their respective effects.

Model (1): GDP_it = α_i + β₁ CPI_it + β₂ VIX_it + β₃ WTI_it + β₄ GOLD_it +

+ β₅ BTCR_it + ε_it , (2)

Model (2): GDP_it = α_i + β₁ CPI_it + β₂ VIX_it + β₃ WTI_it + β₄ GOLD_it +

+ β₅ BTCV_it + ε_it , (3)

Model (3): GDP_it = α_i + β₁ CPI_it + β₂ VIX_it + β₃ WTI_it + β₄ GOLD_it +

+ β₅ ETHR_it + ε_it , (4)

Model (4): GDP_it = α_i + β₁ CPI_it + β₂ VIX_it + β₃ WTI_it + β₄ GOLD_it +

+ β₅ ETHV_it + ε_it , (5)

Model (5): GDP_it = α_i + β₁ CPI_it + β₂ VIX_it + β₃ WTI_it + β₄ GOLD_it +

+ β₅ BTCR_it + β₆ ETHR_,it + ε_it , (6)

Model (6): GDP_it = α_i + β₁ CPI_it + β₂ VIX_it + β₃ WTI_it + β₄ GOLD_it +

+ β₅ BTCV_it + β₆ ETHV_it + ε_it . (7)

Here, β₁–β₆ are the estimated coefficients showing the marginal effects of each independent variable on GDP growth. The sample covers 14 countries over the period Q3 2015– Q3 2023 (32 quarters).

As a proxy for economic performance, GDP_it represents real GDP growth (%). BTCR_it and ETHR_it denote the rate of return on Bitcoin and Ethereum (%), while BTCV_it and ETHV_it represent their respective trading volumes (% change). WTI and GOLD capture energy and commodity price variations, VIX measures market uncertainty, and CPI proxies inflation.

Before estimating the panel regression model, several diagnostic and specification tests were performed, including checks for cross-sectional dependence, slope homogeneity, and unit-root properties. After these steps, the panel cointegration and Granger-causality tests were conducted to evaluate both long-run and short-run relationships among the variables.

The panel GM-FMOLS estimation results for the six models (Table 7) provide insights into the relationships between GDP growth and the explanatory variables.

The impact of Bitcoin trading volume is particularly noteworthy. In Models 2 and 6, BTCV negatively and significantly affects growth, highlighting the potential risks associated with cryptocurrency markets. High Bitcoin trading volumes may signal speculative surges that increase financial uncertainty and potential losses, thereby discouraging investment and economic activity. This result aligns with concerns about the destabilizing role of speculative trading in emerging digital-asset markets.

In contrast, Ethereum trading volume shows a positive and significant effect on growth in Models 4 and 6. This pattern suggests that the ETH market may be perceived differently from BTC, possibly reflecting market innovation and a broader integration of Ethereum-based applications into economic activity. The positive association between ETHV and growth implies that rising activity in the Ethereum ecosystem may accompany expansion in productive investment and technology-driven sectors.

Regarding returns, the RoR of Bitcoin (BTCR) is positively and significantly related to growth in Models 1 and 5. Higher Bitcoin returns appear to stimulate aggregate output, possibly through wealth effects or investment spillovers. Similarly, the RoR of Ethereum (ETHR) positively influences growth in Models 3 and 5, reinforcing the view that favorable cryptocurrency performance can coincide with improved macroeconomic outcomes. Collectively, these results indicate that cryptocurrency markets can contribute positively to economic ­development when returns are high, but elevated trading activity — particularly in Bitcoin — may introduce volatility that dampens stability.

Across all models, CPI shows a consistent and significant positive relationship with growth, suggesting that moderate inflation, as captured by consumer-price changes, coincides with stronger output. This outcome may reflect expansionary conditions where rising prices accompany higher aggregate demand. It also underscores the dual role of inflation—as both a potential stimulus to activity and a variable that must be carefully managed by policymakers to avoid overheating.

The VIX, often termed the “fear gauge,” exerts a negative and significant effect on growth in every model. Higher VIX levels, which capture global financial uncertainty, are associated with lower GDP growth, consistent with the view that market volatility discourages investment and consumption. These findings reinforce the broader economic principle that stability supports growth, while uncertainty constrains it.

Energy-market dynamics, represented by WTI, exhibit a uniformly positive and highly significant relationship with growth. This pattern suggests that higher oil prices benefit oil-exporting economies in the sample and may also reflect global demand expansion during growth periods. The positive link highlights how energy prices influence aggregate activity through both revenue channels and investment in energy-related industries.

Gold prices (GOLD) are positively associated with GDP growth in most models, although the effect in Model 2 is statistically insignificant. The generally positive coefficients may indicate that gold, while often viewed as a safe-haven asset, also contributes to economic stability and investor confidence during uncertain periods. Thus, fluctuations in gold prices can serve as a proxy for shifts in market sentiment and the broader macro-financial environment.

Overall, the empirical evidence suggests that cryptocurrency variables (BTCR, BTCV, ETHR, ETHV) interact with traditional macroeconomic indicators in a manner consistent with partially integrated financial markets. Favorable ­returns are growth-enhancing, whereas excessive trading activity — particularly in Bitcoin — may amplify volatility risks. Macroeconomic variables (CPI, VIX, WTI, and GOLD) behave as expected, confirming that the model captures both financial and real-sector linkages across the 14-country panel.

4.2. Panel Granger-causality test

To explore causal link between variables, the Dumitrescu and Hurlin (2012) panel Granger-causality test was employed. This approach identifies the direction of predictive relationships between variables while allowing heterogeneity across countries. The findings — presented in Appendix A — show both unidirectional and bidirectional causal links. Fig. 2 summarizes the principal causality flows between cryptocurrency indicators and GDP growth.

Fig. 2.

Causality relationships between cryptocurrency variables and GDP growth.

Note: → = unidirectional causality; ↔ = bidirectional causality. Source: Authors’ calculations.

The results reveal several key short-run relationships. BTCR exhibits unidirectional causality toward CPI, implying that Bitcoin returns can help predict inflationary movements. At the same time, causality runs from VIX to BTCR, indicating that Bitcoin performance responds to shifts in market sentiment and financial stability. Moreover, a bidirectional relationship between BTCR and WTI underscores the mutual influence between cryptocurrency and oil markets.

For Ethereum, ETHR shows unidirectional causality from GDP and VIX, suggesting that macroeconomic growth and financial-market volatility drive short-term Ethereum returns. In addition, ETHR responds to changes in oil prices (WTI), ­emphasizing the relevance of energy-market conditions for cryptocurrency dynamics.

The analysis of trading volumes yields further insights. BTCV demonstrates bidirectional causality with GDP and WTI, implying a two-way feedback mechanism between Bitcoin market activity, real-sector performance, and global energy prices. BTCV also shows unidirectional causality toward VIX and GOLD, suggesting that Bitcoin trading intensity can forecast movements in market volatility and gold prices.

In contrast, ETHV exhibits unidirectional causality from GDP and WTI, highlighting the influence of economic growth and energy conditions on Ethereum trading activity. Additionally, ETHV shows bidirectional causality with GOLD, reflecting their interplay as alternative investment assets.

Taken together, these findings illustrate that cryptocurrency variables and macroeconomic indicators are closely interconnected. Bitcoin appears more sensitive to financial-market volatility, while Ethereum’s linkages seem stronger with real-economic and commodity-market dynamics. The combination of bidirectional and unidirectional relationships underscores the dual role of cryptocurrencies — as both transmitters and receivers of macro-financial shocks within the global economic system.

The analysis presented in this study underscores the intricate interdependencies between macroeconomic indicators and cryptocurrencies. The regression results for Bitcoin’s impact on growth are broadly consistent with previous studies­ (Jati et al., 2022; Panigrahi, 2023). According to the findings, Bitcoin’s rate of return (BTCR) contributes positively to GDP growth, suggesting that increases in Bitcoin value support economic expansion, while Bitcoin’s trading volume (BTCV) — a proxy for volatility — exerts a negative effect. Similarly, Ethereum’s rate of return (ETHR) has a positive impact on growth, whereas its trading volume (ETHV) is associated with higher volatility and potential downside risk. These results highlight that cryptocurrency returns can enhance growth when markets are stable, but their volatility poses substantial macro-financial risks.

The bidirectional causality between BTC and GDP further suggests mutual feedback: economic conditions influence Bitcoin’s market performance, while fluctuations in Bitcoin activity affect real economic outcomes. Consequently, policymakers should develop regulatory frameworks to stabilize cryptocurrency markets, and investors should adopt diversified portfolio strategies to mitigate volatility-related risks.

Policymakers should also design a comprehensive legal framework classifying cryptocurrencies as investment assets to channel their growth potential into the formal economy. Such measures would enhance legal clarity, improve investor protection, and ensure proper tax compliance. Establishing clear standards for different cryptocurrency categories could reduce market uncertainty and promote responsible trading practices (Al-Qahtani and Albakjaji, 2023). Implementing licensing and supervision for crypto exchanges — alongside stronger anti-money-laundering (AML) and counter-terrorism financing (CTF) regulations — would further enhance market integrity.

In the short term, authorities can deploy real-time market surveillance, conduct public-education campaigns, and introduce temporary transaction limits during­ extreme volatility. These actions could mitigate systemic risk and prevent economic downturns linked to crypto-market collapses (Bouoiyour et al., 2019; Ünvan, 2019; Sami and Abdallah, 2020; Moussa et al., 2021).

Inflation also exhibits a lasting influence on GDP growth, confirming the exist­ence of a long-run equilibrium relationship. This suggests that policy­makers should prioritize inflation-targeting frameworks to maintain price stability, while investors should closely monitor inflation trends to inform asset-allocation decisions. The negative coefficients for VIX indicate that heightened financial-market volatility correlates with slower growth. The confirmed cointegration implies that sustained volatility can have persistent effects on economic performance. The bidirectional causality between GDP and VIX ­further indicates that shifts in macroeconomic activity influence market volatility and vice versa. To minimize volatility spillovers, policymakers should promote macro-financial stability, and investors should account for volatility indices in portfolio-risk management.

The significant effects of WTI crude oil and GOLD prices on growth emphasize the importance of commodity markets in overall economic performance. Policymakers should therefore consider commodity-price dynamics when designing fiscal and monetary policies, while investors can use oil and gold as hedging instruments against macroeconomic risk.

Encouraging economic diversification toward sectors less dependent on commodities, such as technology and manufacturing, can further strengthen resilience. In the short term, mechanisms like hedging contracts, stabilization funds, and flexible policy adjustments can help moderate the impact of commodity-price fluctuations on the broader economy.

This study examined the dynamic relationships between economic growth, inflation, financial-market volatility, commodity prices, and cryptocurrency metrics­ — specifically the trading volumes and returns of Bitcoin and Ethereum — using quarterly panel data for 14 countries from Q3 2015 to Q3 2023. Unlike previous studies, it compared the top two cryptocurrencies simultaneously to evaluate their combined and distinct effects on economic performance.

Panel-data regression was implemented with cross-sectional dependence and second-generation unit-root tests. Short-run relationships were further analyzed using panel Granger-causality tests. The empirical results confirmed significant cross-sectional dependence, justifying the use of advanced estimators. The GM‑FMOLS results demonstrated robust relationships between GDP growth and macroeconomic indicators, including a notable influence of cryptocurrency metrics. The presence of cointegration supports the existence of a long-run equilibrium among the variables, reflecting the interconnectedness of global financial and real sectors.

Causality analysis revealed bidirectional relationships between Bitcoin trading volume (BTCV) and GDP, and unidirectional causality from GDP to both Ethereum’s rate of return (ETHR) and trading volume (ETHV). Additional findings include unidirectional causality from VIX to growth, from growth to oil prices, and bidirectional causality between growth and gold prices. The sensitivity of cryptocurrency returns to financial volatility and the unidirectional causality from Bitcoin returns (BTCR) to inflation highlight the growing integration of digital assets into the macroeconomic system.

These results confirm that cryptocurrencies and growth are interlinked, carrying important policy implications. To mitigate the destabilizing effects of crypto-market volatility, policymakers must establish robust regulatory frameworks. Integrating cryptocurrency oversight into broader monetary-policy strategies can enhance financial stability and strengthen inflation-targeting regimes. For investors, incorporating cryptocurrencies into diversified portfolios may offer effective hedging opportunities, but such exposure requires disciplined risk-management practices.

Overall, the findings emphasize the complex and evolving relationships among macroeconomic variables, cryptocurrency markets, and GDP growth. The positive effects of Bitcoin and Ethereum returns point to a potentially constructive role for digital assets in economic development. However, the adverse effects of cryptocurrency volatility highlight the inherent uncertainties of this market. Understanding these dual dynamics is crucial for both policymakers and investors.

Future research could expand on these findings by employing dynamic panel estimators that integrate additional dimensions such as market sentiment indices­, technological innovation, and financial inclusion metrics to further clarify the multifaceted role of cryptocurrencies in global economic systems.