The research, conducted by Oumaima Abouzaid and Faouzi Boussedra of Université Chouaib Doukkali in El Jadida, Morocco, begins from a diagnosis that will resonate with anyone who watched economic predictions collapse during recent crises. Traditional econometric tools—ARIMA models, Vector Autoregressions, Dynamic Stochastic General Equilibrium frameworks—rest on assumptions of parameter stability and largely linear relationships. When structural breaks occur, meaning fundamental shifts in the statistical properties governing economic variables, those assumptions fail. Parameters estimated under one regime become biased in the next, statistical inference weakens, and forecast errors balloon precisely when accurate predictions matter most.

To confront this instability, the authors built their framework around a sequence of complementary techniques. First, Bai–Perron structural break analysis detects endogenous breakpoints in the data without requiring prior assumptions about when changes occur. Second, Markov-Switching models identify latent economic regimes—expansionary and recessionary states with distinct statistical properties—whose labels are then fed into the forecasting engine as additional state-dependent features. Only after this regime-aware preparation do the machine learning models take over, trained on data that explicitly encode the economy’s shifting character rather than pretending it never changes.

The centerpiece of the predictive architecture is Double Machine Learning, or DML, a causal inference framework that integrates machine learning algorithms into econometric estimation while preserving valid statistical inference in high-dimensional settings. DML relies on orthogonalization and sample-splitting procedures to estimate causal effects while controlling for large numbers of confounding variables, reducing sensitivity to overfitting and omitted variable bias. Because macroeconomic time series exhibit serial dependence and volatility clustering, the authors adapted the standard approach by using block cross-fitting—partitioning observations into contiguous time blocks rather than random splits—and by computing heteroskedasticity and autocorrelation consistent standard errors with Newey–West corrections. Alongside DML, the framework employs Causal Forests, which extend Random Forest methods to estimate heterogeneous treatment effects across different economic regimes, recognizing that the same policy variable may exert very different influences in calm periods than in crises.

The empirical analysis drew on a monthly macroeconomic dataset spanning January 2000 to March 2025, yielding 303 observations per variable. The dataset combined indicators from the Federal Reserve Economic Data service, the International Monetary Fund, the OECD, the World Bank, and the Economic Policy Uncertainty database. The target variable was inflation, measured as year-over-year Consumer Price Index growth, with explanatory variables including GDP growth, unemployment, the federal funds rate, the dollar exchange rate index, Brent oil prices, M2 money supply growth, the VIX volatility index, and the Economic Policy Uncertainty index. Missing observations, which made up less than 2.3 percent of the data, were imputed using Kalman smoothing, and extreme outliers were winsorized at the first and ninety-ninth percentiles. The data were split chronologically into training, validation, and test subsets to avoid look-ahead bias, with rolling-window cross-validation used for tuning.

The results were striking. Across root mean squared error, mean absolute error, and mean absolute percentage error, the DML framework outperformed every benchmark, including conventional Vector Autoregressions, time-varying parameter VARs, Random Forests, XGBoost, and Long Short-Term Memory neural networks. The DML model achieved an RMSE of 1.01, compared with 1.58 for the standard VAR and 1.12 for the LSTM network. The gap widened during crises. Averaged across the 2008 financial crisis, the COVID-19 shock, and the 2022 inflation surge, the DML framework posted an RMSE of 1.49, against 2.55 for the VAR and 1.70 for the LSTM. The pandemic period proved the hardest test for all models, reflecting unprecedented volatility and nonlinear disruption, yet the causal framework remained the most resilient throughout. Robustness checks with alternative data splits and samples excluding individual crisis episodes confirmed that the results were not artifacts of any particular configuration.

Beyond raw predictive accuracy, the causal estimates offered economically meaningful insights into what drives inflation. Oil prices exerted the strongest positive causal effect, at 0.52, underscoring the dominance of energy price transmission during supply-side shocks. Money supply growth followed at 0.41, suggesting that expansionary liquidity conditions amplify inflationary pressure, while exchange rate depreciation contributed 0.31 through imported inflation channels. Economic Policy Uncertainty and financial volatility added positive effects of 0.24 and 0.18 respectively, indicating that uncertainty shocks and financial stress worsen price instability. Interest rate shocks, by contrast, showed a significant negative effect of −0.38, consistent with conventional monetary transmission theory in which higher policy rates dampen aggregate demand. SHAP-based explainability analysis then revealed how these influences shift across regimes: monetary variables dominate predictions in stable periods, while uncertainty indicators and financial volatility become far more influential during crises.

The framework’s most unconventional element is its blockchain layer, which the authors are careful to distinguish from the predictive engine itself. Blockchain here is not a forecasting tool and was not evaluated with performance metrics. Instead, it functions as a decentralized governance infrastructure. After preprocessing, each dataset version receives a cryptographic fingerprint using the SHA-256 hashing algorithm, and only that hash plus descriptive metadata is recorded on a distributed ledger. Smart contracts validate metadata consistency before new records are appended, and the ledger tracks model training events, hyperparameter configurations, software versions, random seeds, prediction outputs, and even the SHAP explanations attached to each forecast. Because any modification to the underlying data produces a different hash, tampering becomes immediately detectable, and every forecast can be traced back to the exact dataset and model version that produced it.

The authors’ comparative assessment suggests the governance gains are substantial: auditability, traceability, reproducibility, and tamper resistance all improve markedly relative to conventional centralized pipelines, where data revisions and model updates may go partially undocumented. The architecture could also strengthen coordination among central banks, finance ministries, statistical agencies, and international organizations by giving them a shared, transparent validation protocol. The authors acknowledge real trade-offs, however. Cryptographic hashing, smart-contract execution, and network synchronization impose computational overhead, scalability challenges emerge with many participating nodes, and consortium governance demands careful rules for access control and consensus. They also emphasize that the governance benefits described are architectural capabilities rather than experimentally validated performance improvements, since blockchain-specific metrics such as latency and throughput were not empirically tested.

The study’s implications reach well beyond academic benchmarking. As financial systems digitalize and central banks explore Central Bank Digital Currencies, the demand for forecasting systems that are simultaneously accurate, interpretable, and institutionally trustworthy will only grow. By demonstrating that causal machine learning can outperform both classical econometrics and black-box deep learning under structural instability—and that blockchain can wrap such systems in an auditable, tamper-resistant governance shell—the research sketches a plausible blueprint for the next generation of policy-grade economic intelligence. Future work, the authors suggest, will implement the governance layer on a permissioned blockchain platform and explore integrations with decentralized oracles, federated learning, and transformer-based models. If the vision holds, the economies of the future may be forecast not by opaque algorithms or brittle equations, but by transparent, decentralized systems whose every prediction can be verified, explained, and trusted.

Subject of Research: A blockchain-enabled causal machine learning framework for explainable macroeconomic forecasting under structural instability

Article Title: Decentralized blockchain governance for explainable causal AI in macroeconomic forecasting

Article References: Abouzaid, O., & Boussedra, F. (2026). Decentralized blockchain governance for explainable causal AI in macroeconomic forecasting. Frontiers in Blockchain, 9, Article 1883639. https://doi.org/10.3389/fbloc.2026.1883639

Image Credits: AI Generated

DOI: 10.3389/fbloc.2026.1883639

Keywords: blockchain, causal machine learning, Double Machine Learning, macroeconomic forecasting, explainable AI, SHAP, structural breaks, inflation, economic uncertainty, smart contracts, data governance, monetary policy

The study, led by Andre Amaral of Arden University and Taysir E. Dyhoum of Manchester Metropolitan University, analyzed cross-sectional data from 49 countries with the highest HDI rankings, from Switzerland at 0.962 down to Kuwait at 0.831. Liechtenstein was excluded because key variables were unavailable. The researchers drew on data from the United Nations Development Programme and the World Health Organization, examining nine independent variables: gross domestic product, GDP per capita, inflation, the debt-to-GDP ratio, interest rates, unemployment, average income tax rates, life expectancy, and expected years of schooling. The dependent variable, the HDI, is a composite measure introduced in the 1990 UNDP Human Development Report by Pakistani economist Mahbub ul Haq, combining life expectancy at birth, expected years of schooling, and gross national income per capita on a scale from 0 to 1.

What sets this research apart is its dual methodology. The team deployed Ordinary Least Squares (OLS) regression alongside supervised machine learning techniques, splitting the dataset into a 70 percent training set and a 30 percent test set using the caTools package in R. This random split, a widely recognized approach in machine learning, helps limit selection bias and reduces researcher discretion. The OLS framework estimates each coefficient by minimizing the sum of squared residuals, providing an interpretable measure of each variable’s marginal contribution to HDI while holding all other factors constant. The machine learning component then tests whether the model generalizes to unseen countries, guarding against overfitting, the phenomenon where a model captures random noise rather than genuine signal.

The Pearson correlation matrix revealed striking patterns. HDI correlates most strongly with life expectancy at 0.754, followed by GDP per capita at 0.662 and expected years of schooling at 0.552. Aggregate GDP, by contrast, shows only a weak correlation of 0.162, suggesting that the sheer size of an economy matters far less than how its wealth is distributed per person. The debt-to-GDP ratio shows essentially no correlation with HDI at 0.038, a finding that seems to let governments off the hook regarding fiscal discipline. But the story does not end there.

When the researchers ran the full OLS regression, only three variables emerged as statistically significant predictors of HDI: GDP per capita, life expectancy, and expected years of schooling. GDP per capita, life expectancy, and education were significant at conventional levels, while inflation, the debt-to-GDP ratio, interest rates, unemployment, and average income tax rates were not. The model achieved an adjusted R-squared of 0.8826, meaning it explains nearly 88 percent of cross-country variation in HDI within this high-HDI sample. The residual standard error of 0.01322 on 25 degrees of freedom indicates remarkable precision, and the joint F-statistic of 29.41 decisively rejects the null hypothesis that all slope coefficients are zero.

Diagnostic testing confirmed the model’s validity. A quantile-quantile plot showed residuals approximating a normal distribution with only mild tail deviations. The Breusch-Pagan test for heteroskedasticity returned a p-value exceeding 0.05, meaning the researchers failed to reject the null hypothesis of constant error variance, a desirable outcome that strengthens confidence in the standard errors and associated inference. A residuals-versus-leverage plot confirmed that no observation exceeded Cook’s distance threshold, ruling out influential outliers that could distort coefficient estimates. The model then achieved outstanding precision on the test data, with predicted HDI values closely matching actual figures for countries such as Switzerland, Denmark, Germany, and Japan.

But the most intriguing finding lies in what statisticians call suppression effects. A suppressor variable has a weak or non-significant direct relationship with the dependent variable, yet when included in the regression model, it increases predictive power by removing unexplained variance. The team tested whether inflation, unemployment, interest rates, and the debt-to-GDP ratio act as suppressors by comparing the GDP per capita coefficient with and without each suspected variable. The results were remarkable: the interest rate and the debt-to-GDP ratio both act as suppressor variables, strengthening the GDP per capita coefficient when included, while inflation and unemployment do not. When the interest rate was included, the GDP per capita coefficient rose from 0.000000441 to 0.000000445. When the debt-to-GDP ratio was included, it rose from 0.000000439 to 0.000000445.

This suppression analysis reveals that fiscal and monetary policies influence human development through channels that simple bivariate analysis cannot detect. The debt-to-GDP ratio, though showing no direct correlation with HDI, operates indirectly through its relationship with GDP per capita. High debt can constrain fiscal space, increase financing costs and risk premia, and reduce private investment and income growth, thereby lowering the per-capita income that drives HDI. Similarly, interest rates and inflation, though not directly significant, negatively affect GDP per capita, which is in turn moderately correlated with HDI. The researchers also found evidence of indirect links between the debt-to-GDP ratio, inflation, unemployment, and HDI: these variables are negatively correlated with GDP per capita, meaning increases in them may be associated with lower per-capita income and, through that channel, lower human development.

The study’s practical implications are significant. The researchers recommend prudent fiscal policies aimed at reducing government debt to levels that limit inflation risk and support low, stable interest rates. Notably, they challenge the view that rising debt improves HDI, suggesting instead that sustainable investment in public services should be pursued alongside fiscal balance. Their data show that high-HDI countries maintain an average debt-to-GDP ratio of 57 percent, compared with 71 percent for lower-ranked countries, and they advise keeping the ratio below 60 percent. The team also demonstrated the model’s predictive utility by projecting the United Kingdom’s HDI under hypothetical scenarios: if GDP per capita increased by 3 percent and life expectancy and years of schooling each increased by 2 percent, the UK’s HDI would improve from its current 0.93, potentially raising its rank from 17th.

The research also highlights the importance of considering both direct and indirect relationships when evaluating macroeconomic policy impact. By integrating interpretable statistical approaches with machine learning techniques, the study provides a comprehensive assessment of human development determinants that earlier single-country or GDP-focused studies missed. The authors point to several directions for future work, including expanding the dataset to incorporate technological innovation, income distribution, and social capital, conducting longitudinal analyses to clarify causal links over time, and deploying advanced machine learning methods such as decision trees, random forests, and neural networks to detect non-linear patterns that traditional regression may overlook. For now, the message is clear: in the world’s most developed nations, the path to higher human development runs through per-capita prosperity, health, and education, with fiscal discipline and monetary stability operating as essential but hidden enablers behind the scenes.

Subject of Research: Statistical and machine learning analysis of fiscal and monetary policy determinants of human development in high-HDI countries

Article Title: Statistical and machine learning analysis of fiscal and monetary policy determinants of human development in high-HDI countries

Article References: Amaral, A., Dyhoum, T. E., Alqahtani, M., Alharthi, A. S., & Aljohani, H. M. (2026). Statistical and machine learning analysis of fiscal and monetary policy determinants of human development in high-HDI countries. Frontiers in Human Dynamics, 8, Article 1874322. https://doi.org/10.3389/fhumd.2026.1874322

Image Credits: AI Generated

DOI: 10.3389/fhumd.2026.1874322

Keywords: Human Development Index, GDP per capita, fiscal policy, monetary policy, machine learning, OLS regression, public debt, inflation, interest rates, life expectancy, education, suppressor variables

The research, published in Frontiers in Sustainable Food Systems, assembles a balanced panel of 3,672 country-month observations spanning Latin America, Asia, and the Europe-Middle East-Africa region. Countries were selected using strict criteria: they had to be classified as emerging markets by MSCI or the International Monetary Fund, operate floating rather than pegged exchange rate arrangements, avoid centrally administered food prices, and offer complete monthly data. Crucially, none of the selection rules conditioned on realized inflation behavior, which guards against the sample being rigged in favor of the study’s conclusions.

The methodological architecture is deliberately layered. In the first stage, Unuvar estimates a separate distributed lag regression for each country, regressing monthly food inflation on twelve lags of currency depreciation alongside six lags of Brent crude oil prices, the FAO Food Price Index, and urea fertilizer prices. Standard errors use the Newey-West correction for heteroskedasticity and autocorrelation up to twelve months. This country-by-country approach is not a stylistic choice: formal tests rejected slope homogeneity across the panel, and because global price series take identical values in every country each month, they would be perfectly collinear with time fixed effects in any pooled specification. Only country-level regressions allow the oil, food, and fertilizer channels to be separately identified.

The headline numbers are striking. The mean cumulative exchange rate pass-through coefficient is 0.213, meaning a ten percent currency depreciation over twelve months raises consumer food prices by roughly 2.1 percent on average. But the mean conceals extraordinary heterogeneity: estimates range from essentially zero in Indonesia to 0.807 in Türkiye, where pass-through is nearly four times the group average. Hungary follows at 0.505, with Chile and Colombia near 0.34. Only seven of the seventeen coefficients are individually significant at the five percent level, a reminder that any study reporting a single pooled elasticity for emerging markets describes a central tendency that applies to very few actual economies.

By contrast, the oil channel is remarkably weak at policy-relevant horizons. The cumulative effect of crude oil prices on consumer food prices is just 0.008 over six months — statistically indistinguishable from zero — and the entire oil-only specification explains roughly 0.4 percent of within-country variation in food inflation. The effect does become detectable at longer horizons, reaching 0.045 at twelve months and 0.064 at eighteen months, suggesting that energy costs seep into retail prices slowly through production and distribution. But the comparison remains lopsided: a ten percent oil price increase is associated with about a 0.5 percent rise in food prices, versus roughly 2.4 percent for an equivalent depreciation. A formal test rejects the equality of the two channels decisively.

Why is the energy-food nexus so muted at the consumer level? The study points to the structure of retail food prices, which bundle the raw commodity with processing, packaging, distribution, rent, and labor costs. In the United States, the farm share of the consumer food dollar was just 11.8 cents in 2024, with energy entering as one of twelve supply-chain industry groups rather than a dominant cost. Strong oil-commodity relationships observed at the wholesale level are therefore diluted before they reach the checkout. The exchange rate, by contrast, hits from multiple directions simultaneously: it raises the local-currency cost of imported fertilizers, seeds, and fuel, while also lifting the domestic prices of tradable agricultural goods outright.

The study’s most consequential finding concerns state dependence — the idea, originating with John Taylor’s 2000 work, that pass-through is not a fixed structural parameter but depends on the prevailing inflation environment. When inflation is low and expectations are anchored, firms treat cost shocks as temporary and absorb them into margins. When inflation is perceived as persistent, the same shock is passed through rapidly and almost entirely. Using a threshold specification based on observed twelve-month food inflation, the study finds cumulative pass-through of 0.147 below a fifteen percent annual inflation threshold and 0.541 above it — a difference of 0.393 that survives a Bonferroni correction for multiple testing. In practical terms, a ten percent depreciation raises food prices by about 1.5 percent in calm conditions but by approximately 5.4 percent when food inflation is already elevated.

Formal threshold estimation sharpens the picture. Applying Hansen’s panel threshold method, the transition point is estimated at 7.56 percent annual food inflation using overlapping six-month windows, and 6.83 percent with non-overlapping windows, with bootstrap p-values below 0.001 in both cases. Pass-through roughly triples across this divide, rising from about 0.07 to 0.23. Quantile regressions with country fixed effects corroborate the pattern from a different angle: cumulative pass-through climbs from 0.120 at the twenty-fifth percentile of the conditional food inflation distribution to 0.277 at the ninetieth, with equality strongly rejected. Notably, the oil and global food price channels show no comparable regime dependence, suggesting the state-dependent behavior is specific to the exchange rate transmission mechanism.

A skeptic might object that the high-inflation states are simply concentrated in countries with structurally stronger pass-through, such as Türkiye, rather than reflecting a within-country behavioral shift. The study confronts this directly with mean-group estimates that compare inflation states within each economy. The estimated difference is positive in all eight economies where both states can be reliably estimated, and the mean-group difference of 0.339 remains significant even after excluding Türkiye, which contributes fifty-five of the 190 high-inflation months. Kazakhstan, Romania, and Poland — countries with modest average pass-through — show some of the largest state-dependent jumps. Meanwhile, structural exposure indicators such as food import dependence, energy dependence, and fertilizer import dependence fail to explain cross-country variation, with the apparent link between average inflation and pass-through collapsing entirely once Türkiye is removed from the cross-section.

The policy implications are pointed. If exchange rate transmission is the dominant channel, then export bans and trade restrictions are misguided levers: they depress producer incomes and risk supply contractions without touching the mechanism that actually drives prices. Instead, the findings elevate macro-financial stability — exchange rate stability, anchored inflation expectations, and hedging instruments for importers — to the center of food price policy, with agricultural subsidies playing only a complementary role. The results also align with the United Nations Sustainable Development Goal 2.c, which targets the proper functioning of food markets and limits on extreme price volatility. As the study notes, regime dependence intensified after 2015, a period encompassing emerging market currency stress, the pandemic, and the war in Ukraine, hinting that weakening monetary credibility may itself amplify the food price consequences of currency turmoil. For the billions of households in emerging economies who spend a disproportionate share of their budgets on food, keeping inflation anchored may be the most effective food security program available.

Subject of Research: Exchange rate pass-through to consumer food prices and its threshold dependence on the inflation environment in emerging market economies

Article Title: Threshold effects in exchange rate pass-through to food prices in emerging market economies

Article References: Unuvar, F. I. (2026). Threshold effects in exchange rate pass-through to food prices in emerging market economies. Frontiers in Sustainable Food Systems, 10, Article 1960606. https://doi.org/10.3389/fsufs.2026.1960606

Image Credits: AI Generated

DOI: 10.3389/fsufs.2026.1960606

Keywords: exchange rate pass-through, food inflation, emerging market economies, oil prices, threshold effects, state dependence, food security, currency depreciation, Taylor hypothesis, quantile regression, panel data, monetary policy

The new center, known as the EEC, is led by Michael Weber, an economist at ESMT Berlin and a recipient of the Alexander von Humboldt Professorship, one of Germany’s most prestigious endowed research awards. According to the announcement, the center examines how households, firms, and financial market participants form expectations about economic developments and how these expectations influence economic behavior. That tripartite focus is significant. Much of the existing literature on expectations has concentrated on one actor at a time, typically households in household surveys or professional forecasters in financial contexts. A center designed from the outset to span all three groups reflects a growing recognition among economists that expectations do not form in isolation, but interact across the economy in ways that can amplify or dampen economic shocks.

The intellectual case for the center rests on a body of research that has transformed macroeconomics over the past two decades. Expectations about the economic future shape many of the decisions made by individuals and businesses: how much they spend or save, whether they invest, and how they plan for the long term. In theoretical models, anticipated inflation, expected income growth, and perceived employment risk all enter the calculus that determines consumption and investment. But empirical work has repeatedly shown that the expectations held by ordinary people diverge sharply from those of professional economists. Households often misjudge inflation levels, react asymmetrically to price increases and decreases, and update their beliefs in response to salient personal experiences rather than statistical aggregates. These deviations matter, because if expectations drive behavior, then systematic biases in expectations can produce systematic patterns in aggregate economic outcomes.

The better expectations are understood, the more reliably researchers can assess how individuals and organizations respond to economic shocks, market developments, and public communication. This point has taken on particular urgency since the inflation surge that followed the pandemic era, when central banks across Europe and North America discovered how difficult it is to gauge what the public actually believes about future prices. Survey measures of inflation expectations rose alongside realized inflation, and policymakers debated whether those beliefs had become unanchored, a scenario in which the public loses faith that inflation will return to target and behaves in ways that make the fear self-fulfilling. The episode underscored a lesson that the EEC’s research agenda takes as a starting point: reliable, high-frequency, well-designed measurement of expectations is not a luxury but a prerequisite for sound economic management.

To examine these dynamics, the center has organized its research program around four key areas. The first concerns households and how individuals form expectations about inflation, income, employment, and broader economic conditions. Research in this area typically draws on large-scale surveys that ask respondents not only for point forecasts but also for the uncertainty they attach to those forecasts, the information they use, and how their answers change when they receive new information. A second area examines how firms form expectations and how those expectations shape investment, hiring, and pricing decisions. Firm-level expectations have attracted growing attention because corporate beliefs about future demand, costs, and interest rates directly determine hiring plans and price setting, the channels through which expectations translate into realized inflation and employment.

The third area is financial markets, where expectations play a central role in investment decisions, asset pricing, and broader market behavior. Financial market participants are, in a formal sense, professional expectation formers, yet research has documented persistent heterogeneity in their beliefs, herding behavior, and sensitivity to framing and communication. The fourth area covers monetary policy and communication, examining how central bank actions and communication influence the economic expectations of households, firms, and financial market participants. This last area connects the center directly to one of the most consequential policy questions of the era: how words from a central bank move beliefs, and why the same message may persuade markets while leaving the broader public largely unaffected.

Weber framed the mission in terms of data. To better understand economic developments, he said, researchers need reliable data on what people and businesses think and how their assessments change over time. With the European Expectations Center, he said, the aim is to collect this data and make it available for research and economic policy debates. The emphasis on data infrastructure rather than on any single study suggests that the center sees itself as a platform for the wider research community. Survey-based expectations research is notoriously resource-intensive: designing questionnaires that avoid framing biases, recruiting representative samples, fielding surveys at sufficient frequency to capture dynamics, and harmonizing measures across countries all require sustained funding and technical expertise that individual research projects rarely command.

Toward that end, the EEC plans to provide the research community with extensive datasets and research resources, including survey datasets, questionnaires and survey instruments, working papers, and research outputs. Making survey instruments openly available is a deceptively important contribution. When researchers use comparable questions across countries and over time, their findings can be pooled, replicated, and extended, accelerating cumulative progress in a field where results have often been fragmented across bespoke surveys. The center also intends to work with partners to support knowledge exchange, develop new research initiatives, and disseminate findings to academic, policy, and practitioner audiences. Its current partner is the Bank for International Settlements, the Basel-based institution often described as the central bank for central banks, whose involvement signals an interest in connecting academic measurement work directly to the policy institutions that depend on it.

The European setting gives the project particular relevance. The euro area presents a distinctive laboratory for expectations research, because monetary policy is set at the supranational level while economic conditions vary considerably across member states. Households in different countries may experience the same monetary policy decision very differently, depending on their local inflation rates, housing markets, and labor conditions, and their divergent expectations can complicate the transmission of policy. A European center dedicated to expectations research is positioned to document these differences systematically and to test how communication strategies can be designed to reach heterogeneous publics. The involvement of researchers based at a leading international business school in Berlin also bridges the traditionally separate literatures on household expectations, corporate decision-making, and financial markets.

For the broader public, the stakes of this research agenda are easy to state. Expectations are the hinge between economic news and economic behavior: a headline about inflation matters only insofar as it changes what people believe and therefore what they do. If researchers can measure those beliefs accurately, identify when and why they go wrong, and understand which forms of communication correct them, then forecasts improve, policies become better targeted, and the damaging feedback loops that turn fear into recession or optimism into overheating become easier to manage. The European Expectations Center will not answer those questions on its own, but by building the shared data foundation on which such answers depend, it aims to give economists, central bankers, and policymakers a clearer view of the beliefs that quietly steer the economy.

Subject of Research: Formation of economic expectations among households, firms, and financial market participants and their influence on economic behavior

Article Title: ESMT Berlin establishes European Expectations Center for research on economic expectations

Article References: ESMT Berlin establishes European Expectations Center for research on economic expectations. (n.d.). Original publication

Image Credits: AI Generated

DOI: Not provided

Keywords: ESMT Berlin, European Expectations Center, Michael Weber, economic expectations, inflation expectations, household surveys, firm expectations, financial markets, monetary policy, central bank communication, Bank for International Settlements, economic behavior

The study, published in the journal Discover Sustainability by Emmanuel Bosomtwe and Samuel Gameli Gadzo of the University of Education, Winneba, together with Charles Omane-Adjekum, Joseph Nyame, and Emmanuel Kwasi Agyepong, set out to answer a question that has long hovered at the margins of environmental economics: does the way a country manages its money shape the way it manages its environment? To find out, the team assembled a dataset of 80 quarterly observations spanning 2005 to 2024, a period that covers Ghana’s commodity booms, its debt crises, its inflation surges, and its repeated encounters with International Monetary Fund programs. That twenty-year window gives the analysis unusual depth for a study of a single West African economy.

Methodologically, the researchers chose the classical autoregressive distributed lag model, known in econometrics as ARDL. The technique is prized in macroeconomic research because it can handle variables that move at different speeds and different orders of integration, and it can separate short-run dynamics from long-run equilibrium relationships. In practical terms, the ARDL framework allowed the team to ask whether changes in monetary policy today are associated with changes in ecological footprint in the following quarters, and whether a stable long-run relationship ties the two together across the full sample period. The explanatory, quantitative design means the study is built to test hypotheses rather than to generate narratives, which gives its conclusions a firmer statistical footing than descriptive accounts of environmental change.

The headline finding is an inverse relationship between monetary policy and ecological footprint. In plain language, when the Bank of Ghana tightens monetary conditions, raising policy rates, restraining credit growth, and pulling excess liquidity out of the financial system, the ecological footprint tends to fall. The mechanism the authors describe is intuitive once spelled out. Cheap money fuels consumption and expansion. When borrowing is easy, firms invest in energy-hungry production, households buy more fuel, appliances, and imported goods, and the economy as a whole draws harder on forests, fisheries, cropland, and carbon sinks. Tightening the monetary taps slows that appetite. Credit becomes expensive, speculative expansion stalls, and the pace of resource extraction and unsustainable production eases. Monetary policy, in this reading, acts as an indirect brake on environmental degradation, not because it was designed for that purpose, but because it governs the tempo of economic activity itself.

But the second finding is where the study makes its most distinctive contribution. Institutional quality, the researchers report, has a significant positive moderating effect on the monetary policy-ecological footprint relationship. Moderation is a specific statistical concept: it means institutions do not merely sit alongside monetary policy as a separate influence on the environment, they change how well monetary policy works. Strong governance frameworks amplify the environmental dividends of sound monetary management, while weak ones dilute them. A policy rate hike announced in Accra travels through commercial banks, credit markets, regulatory agencies, and enforcement bodies before it changes any real-world behavior. If those transmission channels are corrupted, politicized, or simply under-resourced, the signal degrades before it reaches the factories, farms, and households whose decisions determine the ecological footprint.

The theoretical implications reach into two established bodies of thought. The authors argue that their results extend Ecological Modernization Theory, the idea that modern economies can decouple growth from environmental harm through technological and institutional innovation, by empirically confirming that the effectiveness of monetary policy in reducing ecological footprint is conditional on institutional capacity. In other words, the greening of the economy is not automatic; it depends on the quality of the machinery that implements policy. The study also advances Institutional Quality Theory by demonstrating that institutions play a moderating role, not just a mediating one, in the transmission between monetary policy and environmental outcomes. A mediator carries an effect from cause to consequence; a moderator determines how strong that effect is in the first place. That distinction matters for anyone designing policy, because it implies that institutional reform is not merely another lever to pull but a multiplier that changes the force of every other lever.

For Ghana specifically, the findings arrive at a consequential moment. The country has battled persistent inflation, currency depreciation, and a debt restructuring program, all of which have kept the Bank of Ghana’s policy rate among the highest in Africa. The study suggests that this monetary tightening, whatever its costs for borrowers, carries an underappreciated environmental side benefit: it discourages the excessive resource utilization and unsustainable production that inflate the ecological footprint. At the same time, the moderating result warns that the benefit is not guaranteed. Ghana’s environmental governance depends on institutions such as the Environmental Protection Agency, and the authors’ analysis implies that the strength of those bodies, and of anti-corruption safeguards more broadly, directly conditions how much environmental protection the central bank’s decisions can purchase.

The policy recommendations that flow from the research are unusually concrete for a macroeconomic study. The authors call on the Bank of Ghana to incorporate measures of environmental sustainability into its monetary policy planning, effectively asking the central bank to treat ecological outcomes as a dimension of its mandate rather than an afterthought. They urge the Ghanaian government to deploy anti-corruption strategies, regulatory interventions, and digital surveillance tools to ensure that both environmental and financial policies are actually adhered to, an acknowledgment that rules on paper mean little without monitoring and enforcement. Most ambitiously, they recommend enhanced coordination among the Bank of Ghana, the Environmental Protection Agency, and the Ministry of Finance, so that monetary policy decisions actively promote green financing and long-term ecological resilience. The vision is of a policy triangle in which financial stability, fiscal planning, and environmental regulation reinforce one another instead of operating in silos.

The broader significance of the study lies in its challenge to how we categorize environmental tools. Climate policy is usually assigned to ministries of environment, carbon pricing schemes, and international treaties, while central banks are confined to inflation targets and financial stability. Yet the Ghanaian evidence suggests that the boundary is porous. Every decision about interest rates and liquidity ripples through consumption, investment, and production, and therefore through the ecological footprint. If tightening monetary policy measurably reduces environmental degradation, and if strong institutions magnify that effect, then central banks in developing economies may be sitting on an underused environmental instrument. The authors stop short of claiming monetary policy can replace fiscal and regulatory measures; rather, they conclude that well-implemented monetary policy can serve as an indirect environmental tool that complements them on the road to sustainable development.

There are, of course, limits to what a single-country time series can prove. Eighty quarterly observations capture one national experience, and the ARDL framework identifies associations within that record rather than randomized causal proof. The study’s own framing is careful on this point, presenting the results as empirical confirmation of a theoretical proposition rather than a universal law. Still, the research opens a line of inquiry that other economies, particularly resource-dependent developing nations with young institutional systems, could profitably pursue. If the Ghanaian pattern holds elsewhere, the implication is quietly revolutionary: the quality of a country’s courts, regulators, and anti-corruption agencies may be as important to its environmental future as any treaty or tax, because those institutions decide whether the levers of macroeconomic policy, once pulled, actually move the world. In an era when every basis point of policy is scrutinized for its effect on prices and jobs, Ghana’s researchers have added a third question to the central banker’s scorecard: what did that rate decision do to the planet?

Subject of Research: The moderating role of institutional quality in the relationship between monetary policy and ecological footprint in Ghana

Article Title: The role of institutions in shaping the effect of monetary policy on ecological footprint in Ghana

Article References: Bosomtwe, E., Gadzo, S. G., Omane-Adjekum, C., Nyame, J., & Agyepong, E. K. (2026). The role of institutions in shaping the effect of monetary policy on ecological footprint in Ghana. Discover Sustainability. https://doi.org/10.1007/s43621-026-04721-5

Image Credits: AI Generated

DOI: 10.1007/s43621-026-04721-5

Keywords: Ghana, monetary policy, ecological footprint, institutional quality, ARDL, Bank of Ghana, Environmental Protection Agency, sustainable development, green financing, Ecological Modernization Theory, environmental economics, anti-corruption

The technical architecture of the e-CNY helps explain why it can serve inclusion goals that conventional digital payments cannot. Unlike decentralized cryptocurrencies, the digital renminbi is a direct liability of the central bank, issued through a two-tier system in which the PBOC distributes the currency to commercial banks and licensed operators, who then place it in the hands of consumers and businesses. This structure preserves the role of existing financial institutions while giving the central bank a unified, state-backed digital cash instrument. The e-CNY is designed to pay for consumer retail and service transactions, civil servants’ wages, utility bills, and company-to-company exchanges, and it has been rolled out through large-scale pilots in cities including Shenzhen, Suzhou, and Shanghai, including lottery-style giveaways such as a three-million-yuan distribution in Suzhou that introduced millions of residents to the new wallet technology.

One of the most technically distinctive features of the e-CNY is its support for offline payment. In trials documented in Shenzhen, users could complete transactions in a ‘touch and pay’ mode even when neither device had network connectivity, using near-field communication between hardware wallets. This matters enormously for inclusion because network coverage, smartphone ownership, and battery life are precisely the barriers that exclude many vulnerable users from mobile payment systems. The PBOC and its corporate partners extended this logic to dedicated hardware: a ‘hard wallet’ card program was launched so that people without smartphones at all could hold and spend digital yuan, and Huawei’s Mate 40 series shipped with a built-in hardware wallet for the currency. Mu Changchun, the head of the PBOC’s digital currency research institute, has publicly framed the goal as providing an inclusive and easy-to-use digital central bank currency, a commitment the study treats as central rather than incidental to the project.

The rural dimension of this strategy is where the study’s findings become most vivid. China’s countryside has historically been underserved by bank branches, a gap that the Postal Savings Bank of China, with its vast network of rural outlets, has long worked to narrow. Pilot reporting from 2021 described tests of the digital yuan in villages, and analysts noted the introduction of a phone-free e-RMB card explicitly aimed at helping the rural poor. The policy context is the CCP’s longstanding ‘sannong’ agenda, referring to agriculture, farmers, and the rural countryside, which has made rural finance a recurring political priority. By pairing the e-CNY with card-based hardware and offline functionality, the PBOC effectively built a payment instrument that works for a farmer without a smartphone just as well as for an urban professional with the latest device.

Elderly citizens form the second major target group, and the design choices here are equally revealing. China’s aging population includes millions of people who find smartphone apps confusing or inaccessible, a problem that became acute as the country’s dominant private payment platforms made daily commerce increasingly app-dependent. The e-CNY’s hardware wallets, simple card interfaces, and offline modes offer a state-backed alternative that does not require downloading an application, managing a password, or maintaining an internet connection. The study emphasizes that the PBOC directed this design orientation during the pilot phase itself, meaning inclusion was not an afterthought bolted onto a finished product but a requirement shaping the engineering from the start. That is a notable departure from the pattern in many countries, where central banks treat inclusion as one consideration among many, and where private fintech innovation tends to serve the connected and creditworthy first.

Why would the world’s second-largest economy invest so heavily in a retail CBDC when it already has near-universal private mobile payments? Chin’s analysis points to a combination of motivations. Domestically, the e-CNY gives the state a payment rail it controls, reducing dependence on a small number of private platform companies and providing a public option in digital money. Internationally, Chinese policymakers and analysts have framed the project in terms of currency competition in the digital era and the long-term prospect of renminbi internationalization, although the study notes that the link between a retail CBDC and international currency status remains debated among Chinese economists themselves. There is also a standards dimension: China has proposed global rules for central bank digital currencies, positioning itself as a rule-shaper at the frontier of the technology rather than a follower.

The international spillovers are already visible. Hong Kong began allowing the digital yuan to be used in local shops in 2024, marking the first time the e-CNY crossed a border for everyday retail use. Chinese technology firms with deep telecommunications infrastructure across Africa have been identified by analysts as potential channels for digital currency adoption on the continent, and commentators have described China as leading Africa’s digital currency race. The Bank for International Settlements and the International Monetary Fund have both published extensive research on CBDCs as a potential new tool in the financial inclusion toolkit, and the e-CNY now serves as the most extensive real-world test case for the propositions in that literature. Agustín Carstens, the BIS general manager, has argued that the design of digital currencies touches on the very nature and soul of money, and China’s experiment is providing the empirical evidence for that debate.

The study also documents the scale and pace of adoption. PBOC governor Yi Gang reported in 2023 that e-CNY transaction volumes were seeing strong momentum, and by that point the pilots had expanded to dozens of cities across multiple provinces, covering payroll disbursements, transit, retail, and government services. The corporate ecosystem involved is broad: more than twenty companies were identified as participants in the underlying DC/EP development effort, spanning state banks, telecommunications operators, and technology firms. This public-private division of labor, in which the central bank owns the currency and the core ledger while partners build wallets, acceptance networks, and hardware, is itself a technical and institutional innovation that other central banks studying CBDC design are watching closely.

Still, the research is careful about limits and open questions. Rural adoption faces real challenges, including digital literacy gaps, merchant acceptance in low-density areas, and the persistence of cash preferences among some users, issues that Chinese industry reporting on the state of the e-CNY in rural areas has highlighted. The study does not claim that the digital yuan has solved financial exclusion, only that the PBOC has directed its design toward that goal with unusual seriousness. Whether offline cards, hardware wallets, and two-tier distribution can overcome the entrenched economics of rural banking remains to be seen, and the interplay between the e-CNY and the dominant private payment platforms is still evolving.

What the research ultimately establishes is that the global frontier of central bank digital currency is no longer theoretical. China has moved from concept to mass pilots to cross-border retail use, and it has done so with an explicit inclusion mandate for the elderly, the rural poor, and the unbanked. For the more than one hundred central banks now weighing their own digital currencies, the e-CNY offers the first large-scale demonstration that a CBDC can be engineered not just for efficiency or control, but as a deliberate instrument for bringing the financially marginalized into the formal economy. How well that engineering holds up in the villages and among the aging millions it was designed to serve will shape the next decade of monetary innovation worldwide.

Subject of Research: China's digital renminbi (e-CNY) central bank digital currency and its role in financial inclusion

Article Title: China’s ‘digital renminbi’ (e-CNY) as financial inclusion: the global frontier of central bank digital currency

Article References: Chin, G. T. (2025). China’s ‘digital renminbi’ (e-CNY) as financial inclusion: the global frontier of central bank digital currency. Global Public Policy and Governance, 5(1), 63-81. https://doi.org/10.1007/s43508-025-00112-0

Image Credits: AI Generated

DOI: 10.1007/s43508-025-00112-0

Keywords: central bank digital currency, digital renminbi, e-CNY, People's Bank of China, financial inclusion, China, offline payments, hardware wallet, rural finance, unbanked, monetary policy, fintech