This article analyzes how international cross-border lending originating from advanced countries responds to the use of loan-to-value (LTV) ratios in advanced, eurozone, and emerging countries from 2000 to 2020. Over the last two decades, countries have implemented and adjusted this prudential tool at different levels and times, thereby differentially affecting cross-border banking activities. The literature recommends international coordination of prudential policies to improve their effectiveness. Using a panel data analysis, we explore the impact of differences in LTV ratios across countries on the dampening of bilateral credit growth. The results provide evidence that the policies of lending and borrowing countries do not always simultaneously mitigate bilateral credit. Tightening LTV ratios in lending countries reduces credit flows, while tightening LTV ratios in borrowing countries tends to increase demand for bilateral credit. The level differential across countries can explain the volume of credit flows, which implies heterogeneous LTV ratios can reduce the ability to manage credit growth across countries. Therefore, an appropriate international coordination framework could support international credit stability; however, differences in financial and economic status, as well as the frequency of policy changes across countries, should also be considered, as they explain the direction and significance of bilateral credit flow.
This paper examines how the share of China-origin information and communication technology (ICT) services value added embodied in European manufacturing exports evolved between 1995 and 2018. The analysis has two aims. First, we document the long-run trajectory of Chinese ICT-services inputs in European manufacturing exports and benchmark it against broader shifts in foreign value added, foreign services content, and the ICT intensity of services embodied in exports. Second, we examine whether major institutional milestones - EU accession waves and the post-2013 Belt and Road Initiative (BRI) era - align with changes in exposure trajectories across European economies, treating these dates as structured reference points rather than as clean causal shocks. The results show a sustained increase in China-origin ICT-services inputs, alongside substantial cross-country and cross-industry heterogeneity. Benchmarking exercises indicate that the increase is not reducible to general servicification alone but reflects a re-weighting within the foreign ICT input basket. At the same time, the turning-point analysis does not support a single common break; the evidence is more consistent with gradual, heterogeneous, and specification-sensitive trajectory differences than with one discrete post-milestone shift. The paper therefore contributes new evidence on the measurement, benchmarking, and heterogeneity of Europe's exposure to Chinese ICT services in manufacturing exports.
Classical analysis of free trade agreements (FTAs) highlights the close connection between trade creation and the benefit of an FTA, and the equally close connection between trade diversion and the potential harm. In this paper, we study a case in which a country that already has one FTA adds another one. We show that the new FTA causes trade to be diverted from its prior FTA partner, an effect that we call 'trade reversion'. In contrast to conventional trade diversion, trade reversion causes neither harm nor benefit to the importing country. Unlike conventional trade diversion, which causes a loss of revenue without any corresponding private sector gain, trade reversion does not affect government revenue. When we look at world welfare, if trade reversion is equal to trade diversion, their two effects cancel out, leading the effect on world welfare to be a simple function of the amount of trade creation and the size of the tariff.
This paper examines the role of exchange rates in explaining house prices in a sample of emerging market economies. We use a panel VAR methodology to understand the economic dynamics in response to exchange rate shocks. Impulse responses show that when the local currency depreciates unexpectedly, GDP is adversely affected, and consumer prices rise sharply in the following quarters. Monetary policy responds by raising interest rates. House prices also rise, but their dynamics are very different from those of consumer prices. The house price response peaks in the second quarter and then follows a slow downward process. In contrast, the response of consumer prices is larger on impact, but this impact lasts shorter than the response of house prices. In addition, the cross-country analysis documents significant pass-through heterogeneity across emerging markets. We also document size and sign asymmetries in the sense that deprecations and larger exchange rate shocks produce stronger pass through than appreciations and smaller shocks, respectively. Overall, the empirical analysis provides supportive evidence for the important role of exchange rates in explaining house prices in emerging economies.
This study examines time-varying spillover dynamics among the clean energy, fossil energy, financial, and technology/telecommunications sectors in T & uuml;rkiye and Egypt amid successive geopolitical and macro-financial shocks. Using daily data from January 2021 to October 2025, a TVP-VAR connectedness framework is employed to capture evolving system-wide, directional, and network-based spillovers across the COVID-19 pandemic, the Russia-Ukraine conflict, and the subsequent Middle East escalation. The results indicate persistently elevated but regime-dependent interconnectedness, with system-wide spillovers tightening sharply during major energy-security shocks and partially normalizing thereafter. Spillover transmission is asymmetric and concentrated: Turkish financial institutions and fossil-energy-linked firms form a dominant transmission core, while Egyptian sectors integrate more selectively and tend to act as net receivers. Clean-energy-linked equities exhibit relatively weaker and less persistent spillover transmission than fossil-energy sectors, suggesting partial resilience; however, their connectedness increases during energy-security crises, indicating that financial decoupling remains incomplete and highly regime-contingent. Technology and telecom sectors occupy peripheral positions, responding to shocks without persistently driving contagion. Exchange rates emerge as key amplifiers of connectedness during crises. Overall, the findings highlight that energy transition resilience in emerging markets depends critically on macro-financial stability and coordinated energy-financial policy frameworks.
This study empirically investigates whether AI-based technology valuation produces different outcomes compared to conventional expert-led evaluation methods. The Korea Technology Finance Corporation (KOTEC), a public institution that supports SME financing through credit guarantees, has implemented an AI-driven valuation system known as KPAS to assess firms' intellectual property. To evaluate the effectiveness of this AI-based approach relative to traditional expert appraisals, we examine changes in two key performance indicators: firm sales, which reflect current business performance, and the Tech Index, which serves as a proxy for innovation capacity. Using a difference-in-differences (DID) framework combined with propensity score matching (PSM), we compare the post-guarantee performance of firms evaluated by each method. The results show that guarantees based on technology valuation improve firm performance overall, and that KPAS substantially reduces the time and cost of evaluation compared to expert appraisal. Despite its lower resource requirements, the AI-based method delivers performance outcomes that are comparable to, or even better than, those of traditional evaluations. These findings remain largely robust across alternative model specifications and offer empirical support for the broader adoption of AI-assisted valuation systems in technology finance.
This study measures the export potential of China's new energy vehicles (NEVs) to 27 European Union (EU) members from 2017 to 2023, and empirically examines the impact of the deepening of environmental provisions in the EU RTAs on the export efficiency. The results demonstrate that the export potential of China's NEVs has not been fully reached. The main reasons lie in the excessively detailed environmental provisions in the EU RTAs, import tariffs, technology, and institutional distances. Even so, the export efficiency of China's NEVs to the EU has increased from 0.87 in 2017 to 0.94 in 2023, mainly due to the improvement of infrastructure represented by liner shipping in the EU, and the narrowing of the technology and institution gaps between China and the EU members. Mechanism tests reveal that the deepening of environmental provisions in RTAs would reduce export efficiency by increasing production costs and compliance costs of enterprises, but also enhance export efficiency by stimulating innovation. However, the cost-restraining effect outweighs the innovation-stimulating effect. China needs to introduce environmental provisions in more RTAs, increase the number of environmental issues, enhance the enforceability of environmental provisions to further improve the export efficiency of China's NEVs in the future.
This study examines how geopolitics affect trade and investment among members of an economically cooperative trading bloc. We find that heightened geopolitical tensions dampen trade and cross-border investments within the bloc, with asymmetric effects. Geopolitical frictions reduce goods, but not services trade. Portfolio investment is more sensitive than direct investment. Notably, goods trade is more resilient than portfolio investment. The findings underscore that even within a cooperative economic bloc, geopolitical tensions can disrupt economic activity.
A consumer seeking to purchase multiple products may do so at a single store (one-stop shopping) or across multiple stores (multistop shopping). Fixed shopping or search costs rationalize one-stop shopping, which often emerges as the only equilibrium behavior in theoretical models. In reality, however, multistop shopping is common, and evidence suggests that some consumers adopt it to save on expenditures. This paper uses NielsenIQ consumer scanner data to address two questions about multistop shopping: who engages in it, and why. We examine the first by relating households' multistop shopping tendencies to their demographic and economic characteristics, and the second by comparing actual expenditures to those under a counterfactual one-stop scenario. The regression results indicate that multistop shopping is more prevalent among households with lower opportunity costs of time (due to unemployment or old age). Greater heterogeneity in stores' product offerings and higher inflation - when adjusted for changes in shopping lists - is also associated with higher multistop shopping tendencies. The counterfactual analysis further shows that, for most trips, one-stop shopping would entail higher expenditures than the observed multistop trips. However, the magnitude of these savings is modest: households save between 4.7% and 11.5% of daily expenditures through multistop shopping.
Driven by persistent scepticism about FDI, many countries adopt protectionist measures - regulatory restrictions being a key tool. This study argues that such restrictions hinder FDI, raising the key question: do FDI regulatory restrictions impede the flow of outward FDI (OFDI)? Building on Melitz-type heterogeneous firm models [Melitz, M. J. (2003). The impact of trade on intra-industry reallocations and aggregate industry productivity. Econometrica, 71(6), 1695-1725; Helpman, E., Melitz, M. J., & Yeaple, S. R. (2004). Export versus FDI with heterogeneous firms. American economic review, 94(1), 300-316], the study examines how regulatory restriction influences Indian OFDI. Using firm-level bilateral data on Indian OFDI and the OECD's FDI regulatory restrictiveness index, the analysis reveals that OFDI does not respond uniformly to restrictions - equity limits, screening requirements, and personnel rules have varying effects. Additionally, a battery of heterogeneity checks is conducted to examine how foreign ownership decisions, sector-specific and cross-sectoral restrictions, and the North-South divide interact with regulatory barriers to influence OFDI. Results show that service-sector restrictions are particularly deterrent. The findings confirm strong sectoral complementarity and a pronounced North-South divide in regulatory sensitivity.
We analyze the optimal incentive scheme for a central bank in the presence of inflationary bias in discretionary monetary policy when the monetary authority's preferences are private information. In the proposed mechanism, the government designs a menu of contracts such that the central bank's choice reveals its type. As a result, rational private agents correctly anticipate the central bank's inflation behavior and avoid inflation-expectation errors arising from preference uncertainty. Under this screening mechanism, the inflationary bias is eliminated for the central-bank type that places a high value on the contract. By contrast, when this valuation is low, the bias is only partially reduced, unless the transfer to the central bank is costless for the government, in which case the bias is eliminated for both types.
This paper focuses on capturing actual systemic risk connectedness by exclusively employing bitcoin, precious metals, the US dollar index, and other major futures markets. The dataset spans from 01/01/2018 to 31/12/2024. Diebold and Yilmaz's (2012) approach is employed to analyze the spillover effects of returns and volatility before, during, and after the COVID-19 pandemic. The empirical results generally indicate a rapid increase in connectedness, especially after COVID-19 was declared a global health crisis. The return and volatility systems indicate high connectedness and market integration, implying limited opportunities for diversification across the set of futures markets. The return system findings provide compelling evidence of COVID-19's influence across all variables, particularly on Gold and Silver futures. Bitcoin acted as an effective hedger as it is considered less vulnerable to contributions from all others, yet all variables are substantially influenced. Furthermore, Dollar index, S&P500 ETF, Bitcoin, cryptocurrency policy uncertainty, and cryptocurrency price uncertainty function as the highest contributors to the volatility system. Dynamic analysis provides invaluable insights into how the COVID-19 pandemic impacts connectivity levels, particularly as quarantine measures and vaccine roll-out campaigns bring the pandemic under control. Finally, sub-sample analysis confirms the strong impact of COVID-19 on the futures markets.
This paper develops a medium scale New Keynesian dynamic stochastic general equilibrium (NK-DSGE) model for the Ethiopian economy to examine how financial exclusion affects monetary policy effectiveness. The model features heterogeneous households financially included and financially excluded and distinguishes between monopolistically competitive intermediate-good firms and perfectly competitive final-good firms. Using Bayesian estimation with quarterly data from 2000Q1 to 2022Q4, the model evaluates the transmission of monetary policy, preference, and productivity shocks to consumption and labor supply decisions. The results indicate that the effects of an expansionary monetary policy shock unfold gradually, requiring approximately ten quarters for full transmission following a one-standard-deviation reduction in the policy interest rate. Importantly, monetary policy effectiveness declines as the share of financially excluded households increases. Financial exclusion weakens the interest rate transmission channel and reduces welfare gains from stabilization policy. These findings highlight structural constraints that limit policy effectiveness in economies with underdeveloped financialsystems and underscore the importance of financial inclusion for improving macroeconomic stability and social welfare.
This paper investigates the asymmetric out-of-sample predictability of macroeconomic variables for the real exchange rate between the United States and Korea. While conventional models often suggest that the bilateral real exchange rate is primarily driven by the relative economic performance of the two countries, our research highlights the superior predictive power of latent factors obtained from U.S. economic variables, while Korean factors fail to enhance predictability and often act as noise. We attribute the strong predictability of U.S. factors to significant cross-correlations observed among a panel of bilateral real exchange rates vis-& agrave;-vis the U.S. dollar, indicating a limited role for idiosyncratic factors associated with smaller economies. Our major findings are based on data from the pre-COVID19 era. We further explore how economic crises disrupt this relationship, resulting in temporary yet persistent disconnects between the real exchange rate and macroeconomic fundamentals.
This study investigates the extent to which export and import restrictions and governance quality shape an economy's export purchasing power and import capacity as measured by the income terms of trade (ITT) and identifies the determinants of ITT. Despite the growing importance of ITT as a more comprehensive measure of a country's export purchasing power and import capacity, existing literature has largely overlooked the concept, and the relationship between trade restrictions and ITT remains underexplored. Using the two-step system generalised method of moments (GMM) on panel data consisting of 139 countries from 2008 to 2021, results reveal that import restrictions are significantly associated with ITT deterioration, with import licenses, non-tariff measures, and tariffs having relatively sensitive effects. Similarly, export restrictions, especially export licenses, have adverse effects, although the magnitudes are inelastic. Moreover, improvements in governance quality enhance ITT, with control of corruption and regulatory quality playing a substantial role. The results underscore the need for policy strategies aimed at the gradual reduction of trade restrictions, supported by regulatory harmonisation, trade facilitation, and targeted measures to enhance export capacity.
This paper investigates bilateral value-chain participation between Japan and Korea and its association with firm-level value-added. Using firm-level data for both Japanese and Korean manufacturing firms over the period 2006-2014, we find that firms in both countries operating in industries with stronger backward bilateral value-chain linkages of Korea with Japan-or equivalently, forward linkages of Japan with Korea-tend to exhibit higher real value-added, while the opposite direction of participation is associated with lower value-added. These patterns are broadly observed across major manufacturing industries, with the automobile industry exhibiting distinct behavior.
With the development of new machine learning techniques, artificial intelligence is increasingly being used to forecast financial time series. Exchange rates are known to be nonlinear, exhibiting stochastic trends. In part for this reason, many models still fail to outperform a random walk. This paper runs forecasting tests for five major currencies at a monthly resolution, over horizons of 1-4 months. Prior to the forecasting experiments, regressions are run for causal factors. Few are found to be statistically significant. Instead, the exchange rates are dominated by serial correlation. There is also evidence of serial dependence in the rate of change, arguing for including lagged differences in the models. Three basic methods are tested, nonlinear regression, neural networks and support vector machines (SVM). All the models are estimated over moving windows of observations. The tests demonstrate that while it is possible to surpass the accuracy of a random walk, the magnitude of the improvement is small. Further, the accuracy of the machine learning models is only somewhat better than that of the regressions. Finally, given the way in which exchange rates have evolved it is essential to retrain the models as new data becomes available.
This paper examines the association between female education, household energy use, and national carbon emissions in a global panel. Using data from 171 countries over the period 1990-2020, we analyze how female enrollment at the primary, secondary, and tertiary levels is related to two outcomes: the adoption of clean cooking energy and per capita CO2 emissions. Results from fixed-effects, fractional-response, and dynamic panel models indicate that higher female secondary enrollment is consistently associated with greater use of clean cooking fuels and technologies, whereas higher female tertiary enrollment is associated with lower per capita carbon emissions. We further document a positive correlation between female tertiary education and women's representation in parliament, highlighting an institutional context in which education and environmental outcomes co-evolve. Overall, the findings suggest that women's education is systematically associated with broader social and environmental outcomes beyond labor-market indicators, including household energy transitions and sustainable development patterns.
The current study examines whether government-led digital finance initiatives promote firm-level digital innovation by leveraging the staggered rollout of China's Fintech pilot programs as quasi-natural experiments. Our dataset comprises 26,746 firm-year observations of A-share listed companies from 2009 to 2023. To measure innovation, we develop a text-based indicator derived from the frequency of digital-related keywords in the annual reports of the listed firms. Employing a multi-period difference-in-differences design, we find that designation as a pilot zone increases digital innovation intensity by 0.8225 per thousand report words. These results remain robust across parallel, propensity score matching, placebo, and robustness tests. Mediation analysis reveals that the part of the effect is attributable to increased R&D intensity, with the program raising the average R&D-to-sales ratio by 0.24 percentage points. Moreover, program effect is stronger among high-tech firms and those located in Central and Western China, regions characterized by relatively weaker financial and digital infrastructure.
