The research, published in the journal Frontiers in Blockchain by Teboho Charles Mashao of North-West University’s School of Economic Sciences in Potchefstroom and Ireen Choga of the university’s Mafikeng Campus, set out to answer a question that has mostly been addressed through theory and conceptual reviews until now: does blockchain adoption actually drive trade, and if so, how? Using quarterly time series data spanning from the first quarter of 2013 to the final quarter of 2024, the pair applied two closely related econometric techniques, the autoregressive distributed lag (ARDL) model and its nonlinear counterpart, the NARDL, to disentangle the relationship between blockchain activity and trade flows.

The headline finding is unambiguous. Blockchain technology, proxied in the study by Bitcoin transaction volume as the most widely recognized practical application of the underlying distributed ledger, has a positive and statistically significant effect on South Africa’s international trade in the long run. According to the model, a one percent increase in blockchain activity is associated with a 0.0471 percent rise in international trade, measured as the combined value of exports and imports relative to gross domestic product. The short-run coefficients tell the same story, with blockchain activity again showing a positive and significant effect on trade within the quarter.

But the study’s most intriguing contribution lies in what happens when the relationship is allowed to be asymmetric. By decomposing blockchain activity into positive and negative partial sums using the nonlinear ARDL framework developed by Shin, Yu and Greenwood-Nimmo, the researchers discovered that the effects of blockchain on trade are not symmetrical. A positive shock, meaning an increase in blockchain activity, produces a significant and positive long-run boost to trade of roughly 0.0409 percent. A negative shock, by contrast, has a negative but statistically insignificant effect, meaning that dips in blockchain activity do not appear to drag trade down in any meaningful way. The Wald test confirmed that this asymmetry is statistically significant in the long run, marking the first time such nonlinear dynamics have been documented at the country level for blockchain and trade.

The theoretical scaffolding for these results draws on two classic frameworks. Transaction cost theory, originally proposed by Ronald Coase and later developed by Oliver Williamson, holds that economic actors organize their exchanges to minimize the friction costs involved in trading. Blockchain, by removing intermediaries, reducing paperwork, and guaranteeing transparency through a permissioned distributed ledger accessible only to authorized parties, directly attacks those frictions. The technology acceptance model, meanwhile, explains why adoption accelerates: firms and individuals embrace technologies they perceive as useful and easy to deploy, and rising Bitcoin transaction volumes in the study’s data signal exactly that kind of growing engagement with blockchain-based systems.

The practical mechanics of how blockchain facilitates trade are worth spelling out. In the traditional international trade model, an exporter and importer establish a business order, then engage banks to handle letters of credit and bills of lading, with documents, cash, and logistics flowing through a paper-heavy chain that suffers from limited transparency, high costs, and time-consuming authentication. In the blockchain model, a permissioned distributed ledger allows peer-to-peer transactions with real-time information access for all authorized parties. Smart contracts, self-executing agreements coded onto the blockchain, can guarantee payment automatically once pre-agreed conditions are met, cutting both delays and costs while reducing the risk of fraud, counterfeiting, and tampering with trade documents.

Beyond blockchain itself, the study’s control variables produced a mix of expected and surprising results. The exchange rate emerged as the most powerful driver of trade, with a one percent depreciation of the local currency associated with a 0.5299 percent increase in international trade in the long run, a finding consistent with the Marshall-Lerner condition. Foreign direct investment and gross domestic product also showed positive and significant long-run effects, aligning with the gravity model of trade, which holds that larger economies trade more. Inflation, counterintuitively, showed a positive but statistically insignificant long-run relationship, and taxation on international trade was likewise insignificant in the linear model, though it turned negative and significant in the nonlinear specification. The error correction term of −0.2459 indicated that roughly a quarter of any disequilibrium in the trade relationship is corrected each quarter, pointing to a slow but steady adjustment toward equilibrium.

South Africa makes a particularly compelling case study because it sits at the forefront of blockchain innovation on the African continent. Together with Nigeria and Kenya, it accounts for roughly 80 percent of blockchain activity in Africa, with key applications concentrated in insurance, finance, telecommunications, and health. Yet the continent’s blockchain journey has been constrained by limited technical skills, insufficient infrastructure, and high costs, barriers that have prevented many African economies from fully capitalizing on the technology’s economic promise. By focusing on a single country rather than a global aggregate, the study addresses a gap in the literature, since institutional and economic conditions vary so widely that findings from one context cannot simply be generalized to another.

The policy implications are clear. The authors recommend that South African policymakers actively promote blockchain adoption in international trade by crafting supportive regulatory frameworks and investing in blockchain infrastructure. Such measures, they argue, could create a secure, transparent, and efficient trade environment that lowers barriers for all participants and fosters deeper global economic integration. Small and medium-sized enterprises, in particular, stand to benefit from blockchain-enabled trade processes that reduce paperwork and open access to larger customers and suppliers worldwide. The study also calls for policies conducive to foreign direct investment, exchange rate stability, and economic growth, all of which the data show to be meaningful drivers of trade volume.

The research is not without limitations. Bitcoin transaction volume, while the most common proxy in the blockchain literature, represents only one of many applications that the underlying technology enables, and the authors acknowledge that future work should explore other blockchain metrics and alternative estimation techniques. They also suggest extending the analysis to examine blockchain’s effects on exports and imports separately. Still, as one of the first data-driven, country-specific empirical analyses of blockchain and trade, and the first to document asymmetric long-run effects, the study offers a template for how economists can move beyond conceptual enthusiasm and measure, with statistical rigor, whether the blockchain revolution is actually delivering on its promises for developing economies.

Subject of Research: The empirical impact of blockchain technology on international trade in South Africa

Article Title: The effect of blockchain technology on international trade in South Africa

Article References: Mashao, T. C., & Choga, I. (2026). The effect of blockchain technology on international trade in South Africa. Frontiers in Blockchain, 9, Article 1880173. https://doi.org/10.3389/fbloc.2026.1880173

Image Credits: AI Generated

DOI: 10.3389/fbloc.2026.1880173

Keywords: blockchain technology, international trade, South Africa, ARDL, NARDL, Bitcoin, transaction cost theory, foreign direct investment, exchange rate, economic growth, trade policy, distributed ledger

The methodological architecture of the study is what gives its findings their weight. Rather than relying on a single regression model, Salami combines Ordinary Least Squares estimation with Newey–West heteroskedasticity and autocorrelation-consistent standard errors, a correction that ensures the statistical conclusions remain robust even when the data exhibit the volatility typical of developing economies. On top of this baseline, the analysis layers time-series threshold regression, which allows the inflation–growth relationship to shift abruptly at a critical value, and smooth transition regression, which instead models a gradual change in the relationship as inflation rises. The two approaches are complementary: threshold models capture sharp regime changes, while smooth transition models capture softer, more continuous adjustments. Comparing their results provides a way to test whether the inflation–growth link truly behaves differently at different levels of inflation, or whether any apparent shift is a statistical artifact.

The headline result is that inflation is negatively and statistically significantly associated with real GDP growth in Nigeria, and that the magnitude of this association varies across inflation regimes. The threshold model identifies the critical value at 12.22 percent, and, notably, the inflation coefficient is negative both below and above that line. This is an important nuance. Some countries exhibit a benign zone of low inflation where the relationship is positive or neutral, turning negative only at higher rates. In Nigeria’s case, the data suggest that inflation is already a burden even at moderate levels, and that crossing the 12.22 percent mark changes the intensity of the damage rather than its direction. For a country that has experienced inflation rates well into double digits in recent years, this finding implies that the economy has been operating on the wrong side of the threshold for extended periods.

Rigorous science demands sensitivity analysis, and the study delivers one that addresses a subtle data problem. The trade-openness series used in the analysis required a completion procedure for its three boundary observations, and any such imputation could in principle distort the estimated threshold. To check this, the author re-estimated the model excluding those three boundary observations. The result was reassuring: the 12.22 percent threshold survived intact, and the regime-specific coefficients were closely comparable to the baseline estimates. This means the central finding is not materially driven by the data-completion procedure, a conclusion that substantially strengthens confidence in the threshold estimate. In empirical macroeconomics, where results can hinge on small sample choices, this kind of robustness check is the difference between a suggestive correlation and a defensible policy benchmark.

The smooth transition model tells a more cautious story. It identifies a transition location of approximately 11.50 percent, remarkably close to the threshold estimate, but its transition parameters are not statistically significant. In practical terms, this means the evidence for a gradual, smooth adjustment of the inflation–growth relationship is weaker than the evidence for a discrete regime shift. The convergence of the two models on a similar location, around 11.5 to 12.2 percent, is itself informative, suggesting that something real happens to Nigerian growth dynamics as inflation approaches the low teens. But the statistical weakness of the transition parameters means the threshold model remains the more reliable guide, and policymakers should treat the smooth transition results as corroborating rather than independently conclusive.

Inflation does not operate in a vacuum, and the study’s treatment of Nigeria’s external and fiscal environment is one of its most valuable contributions. Exchange-rate depreciation and international Brent crude oil-price shocks are both negatively associated with growth in the baseline specification, a result that reflects Nigeria’s well-known vulnerabilities as an oil-dependent economy with a currency sensitive to external pressures. Intriguingly, however, the independent statistical significance of these external variables weakens once nonlinear inflation dynamics are incorporated into the model. This suggests that part of the damage inflicted by oil shocks and currency depreciation may operate through their effect on inflation itself, which then transmits to growth. In other words, inflation may be the channel through which Nigeria’s external shocks do much of their harm, making price stability not merely one policy goal among many but a central defensive mechanism.

Perhaps the most policy-relevant finding concerns the interaction between inflation and fiscal conditions. The study finds a positive and statistically significant interaction between inflation and the fiscal balance, indicating that improved fiscal positions can attenuate, though not eliminate, the adverse association between inflation and growth. The mechanism is intuitive: when a government’s budget is healthier, it is better able to cushion households and firms against price shocks, avoid inflationary deficit financing, and maintain public investment that supports productive capacity. The crucial caveat is the word attenuate. Sound fiscal management softens the blow of inflation but cannot substitute for monetary control of prices. The finding underscores the study’s overarching conclusion that Nigeria’s growth stability depends on coordinated monetary, fiscal, and exchange-rate policies rather than on any single lever pulled in isolation.

The temporal dimension of the analysis reveals how Nigeria’s macroeconomic history is written into the data. Structural-break tests identify breaks in 2016, 2020, and 2023, corresponding to the country’s recession, the COVID-19-related economic disruption, and the exchange-rate liberalisation reforms respectively. These are not abstract statistical artifacts but recognizable chapters in Nigeria’s recent economic narrative. The 2016 recession followed the collapse of oil prices and foreign-exchange shortages; the 2020 break captures the pandemic’s simultaneous supply and demand shocks; and the 2023 break reflects the liberalisation measures that reshaped the currency regime. That the econometric tests independently detect these episodes lends credibility to the modeling framework, while also reminding analysts that parameter stability cannot be assumed in an economy subject to such repeated structural upheaval.

Complementary evidence comes from quantile regression, which examines how the inflation–growth relationship varies across the distribution of growth outcomes rather than only at the mean, and from machine-learning techniques applied in an explicitly exploratory role. Random Forest and XGBoost models were assessed using chronologically ordered hold-out validation, a demanding test that trains models on earlier data and evaluates them on later periods. The results indicated limited temporal generalisation, meaning the machine-learning models struggled to predict growth in unseen future periods. Consequently, their feature-importance and SHAP outputs are interpreted as exploratory nonlinear evidence rather than independent predictive or causal confirmation. This is a methodologically honest stance. Machine-learning tools can reveal complex interaction patterns that linear models miss, but without demonstrated out-of-sample predictive power, their outputs cannot be elevated to causal claims. The study’s willingness to constrain its own machine-learning findings is a model of restraint in a field often tempted by algorithmic overreach.

Taken together, the findings paint a coherent and sobering picture of Nigeria’s post-shock macroeconomic environment. The inflation–growth relationship is negative, regime-sensitive, and conditioned by fiscal and external circumstances. Trade openness is positively associated with growth, offering a rare bright spot, while broad money growth exhibits a negative baseline relationship, though these effects vary across specifications. Parameter-stability diagnostics, including CUSUM and CUSUMSQ tests, confirm that the baseline model’s coefficients and residual variance remain within statistical bounds, indicating no systematic instability that would undermine the estimates. For Nigerian policymakers, the practical message is clear: keeping inflation below the estimated 12.22 percent threshold, strengthening fiscal balances to buffer the inflation–growth nexus, and managing exchange-rate and oil-price exposures through coordinated policy are not competing objectives but interlocking requirements. For economists studying other emerging economies, the study offers a template for combining classical threshold econometrics with modern machine-learning diagnostics while respecting the limits of each. As global inflation pressures persist, the Nigerian experience serves as a reminder that the cost of letting prices drift upward is not abstract; it is measured in forgone growth, and the price of that mistake becomes steeper with every percentage point past the line.

Subject of Research: The nonlinear threshold relationship between inflation and economic growth in Nigeria

Article Title: Inflation threshold, fiscal conditions and growth stability in Nigeria’s post-shock macroeconomic environment

Article References: Salami, R. A. (2026). Inflation threshold, fiscal conditions and growth stability in Nigeria’s post-shock macroeconomic environment. Discover Sustainability. https://doi.org/10.1007/s43621-026-04682-9

Image Credits: AI Generated

DOI: 10.1007/s43621-026-04682-9

Keywords: inflation, economic growth, Nigeria, inflation threshold, fiscal balance, exchange rate, oil prices, threshold regression, quantile regression, machine learning, macroeconomics, structural breaks